In [1]:
# Creating the entry point for spark 
from pyspark import SparkContext,SQLContext
sc = SparkContext()
sqlContext = SQLContext(sc)

from pyspark.sql import SparkSession
sparkSession = SparkSession.builder.getOrCreate()

# Importing the below modules for data manipulation
from pyspark.sql.functions import count
In [2]:
# To handle matrix & array computations
import numpy as np

# to handle data in form of rows and columns
import pandas as pd

# importing ploting libraries
import matplotlib.pyplot as plt

from scipy.stats import itemfreq

# For visualization
import seaborn as sns

# For train & test split
from sklearn.model_selection import train_test_split

# For plotting inline in Jupyter notebook
%matplotlib inline

# datetime processing
from datetime import datetime

# Data Preprocessing
from sklearn import preprocessing

# For conducting statistical tests
import statsmodels.api as sm
import statsmodels.formula.api as smf

# For creating regression Models using LinearRegression, Stochastic Gradient Descent regression, Decision Tree, RandomForest
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import SGDRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor

# For calculating cross validation score
from sklearn.model_selection import cross_val_score

# For calculating different metrics like R2, mean abolute error and mean squared error
from sklearn.metrics import make_scorer, mean_absolute_error, mean_squared_error

# For identifying and setting best hypertuning parameters for the model
from sklearn.model_selection import GridSearchCV

# For creating decision tree in graphical mode
import graphviz 
from sklearn.tree import export_graphviz

# special matplotlib argument for improved plots
from matplotlib import rcParams
sns.set_style("whitegrid")
sns.set_context("poster")
/usr/local/anaconda/python2/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
  from pandas.core import datetools
In [3]:
# Hypothesis
# Oil price plays a role in the total number of sales transactions & sales
In [4]:
# Loading Merged train daily transactions for store 44
In [5]:
sdfTrain = sqlContext.read.csv("/gl-capstone-data/Team6-C-Sep/Data/train_transactions_daily_store44.csv",header = True,inferSchema = True)
In [6]:
sdfTrain.printSchema()
root
 |-- date: timestamp (nullable = true)
 |-- store_nbr: integer (nullable = true)
 |-- transactions: integer (nullable = true)
 |-- unit_sales: double (nullable = true)

In [7]:
sdfTrain.count()
Out[7]:
1089
In [8]:
print(sdfTrain.show(5))
+-------------------+---------+------------+-----------------+
|               date|store_nbr|transactions|       unit_sales|
+-------------------+---------+------------+-----------------+
|2014-07-02 00:00:00|       44|        4494|50616.44199999999|
|2014-07-03 00:00:00|       44|        3849|        34998.176|
|2014-07-04 00:00:00|       44|        4111|35662.77099999999|
|2014-07-05 00:00:00|       44|        4674|50965.70800000001|
|2014-07-06 00:00:00|       44|        5092|64146.52700000003|
+-------------------+---------+------------+-----------------+
only showing top 5 rows

None
In [9]:
sqlContext.registerDataFrameAsTable(sdfTrain, "sdfTrainTbl")

Merge Train and Oil:

In [10]:
sdfOil = sqlContext.read.csv("/gl-capstone-data/Team6-C-Sep/Data/oil_2013_2015_interpolated.csv",header = True,inferSchema = True)
In [11]:
sdfOil.printSchema()
root
 |-- date: timestamp (nullable = true)
 |-- dcoilwtico: double (nullable = true)

In [12]:
sdfOil.count()
Out[12]:
1095
In [13]:
minRow = sdfOil.agg({"date": "min"}).collect()[0]
print minRow["min(date)"]
2013-01-01 00:00:00
In [14]:
maxRow = sdfOil.agg({"date": "max"}).collect()[0]
print maxRow["max(date)"]
2015-12-31 00:00:00
In [15]:
sqlContext.registerDataFrameAsTable(sdfOil, "sdfOilTbl")
In [16]:
# Merge Training dataset with interpolated oil dataset
In [17]:
sdfTrainWithOil = sdfTrain.join(sdfOil, ["date"],"leftouter")
In [18]:
print(sdfTrainWithOil.show(20))
+-------------------+---------+------------+------------------+------------------+
|               date|store_nbr|transactions|        unit_sales|        dcoilwtico|
+-------------------+---------+------------+------------------+------------------+
|2014-07-02 00:00:00|       44|        4494| 50616.44199999999|            105.18|
|2014-07-03 00:00:00|       44|        3849|         34998.176|            104.76|
|2014-07-04 00:00:00|       44|        4111| 35662.77099999999|          104.6175|
|2014-07-05 00:00:00|       44|        4674| 50965.70800000001|           104.475|
|2014-07-06 00:00:00|       44|        5092| 64146.52700000003|          104.3325|
|2014-07-07 00:00:00|       44|        4013|         37039.867|            104.19|
|2014-07-08 00:00:00|       44|        3643|31809.408000000007|            104.06|
|2014-07-09 00:00:00|       44|        4083|40275.270000000004|            102.93|
|2014-07-10 00:00:00|       44|        3780|29022.608999999997|            103.61|
|2014-07-11 00:00:00|       44|        4250|37861.539999999986|            101.48|
|2014-07-12 00:00:00|       44|        4816| 53790.55700000001|101.56333333333333|
|2014-07-13 00:00:00|       44|        4179| 46165.93300000001|101.64666666666668|
|2014-07-14 00:00:00|       44|        4003| 36774.15199999999|            101.73|
|2014-07-15 00:00:00|       44|        3812|         32841.531|            100.56|
|2014-07-16 00:00:00|       44|        4195| 45677.12500000001|            101.88|
|2014-07-17 00:00:00|       44|        3714|29882.359000000004|            103.84|
|2014-07-18 00:00:00|       44|        4160| 34775.24300000002|            103.83|
|2014-07-19 00:00:00|       44|        4838| 50027.62699999999|104.33333333333333|
|2014-07-20 00:00:00|       44|        4433|50306.461000000025|104.83666666666667|
|2014-07-21 00:00:00|       44|        3940|35054.345000000016|            105.34|
+-------------------+---------+------------+------------------+------------------+
only showing top 20 rows

None
In [19]:
sdfTrainWithOil.count()
Out[19]:
1089
Train has a count of 1089...Train data left outer join with oil

Merge Train and Holiday_events:

In [20]:
sdfHolidayEvents = sqlContext.read.csv("/gl-capstone-data/Team6-C-Sep/Data/holidays_events_2013_2015_noduplicates.csv",header = True,inferSchema = True)
In [21]:
print(sdfHolidayEvents.show(5))
+-------------------+--------+--------+-----------+--------------------+-----------+
|               date|    type|  locale|locale_name|         description|transferred|
+-------------------+--------+--------+-----------+--------------------+-----------+
|2013-01-01 00:00:00| Holiday|National|    Ecuador|  Primer dia del ano|      false|
|2013-01-05 00:00:00|Work Day|National|    Ecuador|Recupero puente N...|      false|
|2013-01-12 00:00:00|Work Day|National|    Ecuador|Recupero puente p...|      false|
|2013-02-11 00:00:00| Holiday|National|    Ecuador|            Carnaval|      false|
|2013-02-12 00:00:00| Holiday|National|    Ecuador|            Carnaval|      false|
+-------------------+--------+--------+-----------+--------------------+-----------+
only showing top 5 rows

None
In [22]:
sdfHolidayEvents.count()
Out[22]:
150
In [23]:
sqlContext.registerDataFrameAsTable(sdfHolidayEvents, "sdfHolidayEvents")
In [24]:
sdfHolidayEvents.printSchema()
root
 |-- date: timestamp (nullable = true)
 |-- type: string (nullable = true)
 |-- locale: string (nullable = true)
 |-- locale_name: string (nullable = true)
 |-- description: string (nullable = true)
 |-- transferred: boolean (nullable = true)

In [25]:
sdfTrainWithOilHolidayEventsJoined = sdfTrainWithOil.join(sdfHolidayEvents, ["date"],"leftouter")
In [26]:
sqlContext.registerDataFrameAsTable(sdfTrainWithOilHolidayEventsJoined, "sdfTrainWithOilHolidayEventsJoined")
In [27]:
sqlContext.sql("select count(*) from sdfTrainWithOilHolidayEventsJoined").show()
+--------+
|count(1)|
+--------+
|    1089|
+--------+

In [28]:
minRow_Train = sdfTrainWithOilHolidayEventsJoined.agg({"date": "min"}).collect()[0]
print minRow_Train["min(date)"]
2013-01-02 00:00:00
In [29]:
maxRow_Train = sdfTrainWithOilHolidayEventsJoined.agg({"date": "max"}).collect()[0]
print maxRow_Train["max(date)"]
2015-12-31 00:00:00
In [30]:
#date_mask = (sdfTrainWithOilHolidayEventsJoined['date'] >= '2015-01-01') & (sdfTrainWithOilHolidayEventsJoined['date'] <= '2015-12-31')
pd_train = sdfTrainWithOilHolidayEventsJoined.toPandas()

#Print the size
len(pd_train)
Out[30]:
1089
In [31]:
pd_train.head(5)
Out[31]:
date store_nbr transactions unit_sales dcoilwtico type locale locale_name description transferred
0 2014-07-02 44 4494 50616.442 105.1800 None None None None None
1 2014-07-03 44 3849 34998.176 104.7600 None None None None None
2 2014-07-04 44 4111 35662.771 104.6175 Event National Ecuador Mundial de futbol Brasil: Cuartos de Final False
3 2014-07-05 44 4674 50965.708 104.4750 Event National Ecuador Mundial de futbol Brasil: Cuartos de Final False
4 2014-07-06 44 5092 64146.527 104.3325 None None None None None

Data Pre-processing

In [32]:
pd_train_nan = (pd_train.isnull().sum() / pd_train.shape[0]) * 100
pd_train_nan
Out[32]:
date             0.00000
store_nbr        0.00000
transactions     0.00000
unit_sales       0.00000
dcoilwtico       0.00000
type            86.77686
locale          86.77686
locale_name     86.77686
description     86.77686
transferred     86.77686
dtype: float64
In [33]:
# There are 86% of Nulls/NA in Holiday events attributes. Replacing Nulls or NA with No_Holiday as the default value
In [34]:
pd_train['type'] = pd_train.type.replace(np.NaN, 'No_Holiday')
pd_train['locale'] = pd_train.locale.replace(np.NaN, 'None')
pd_train['locale_name'] = pd_train.locale_name.replace(np.NaN, 'None')
pd_train['description'] = pd_train.description.replace(np.NaN, 'None')
pd_train['transferred'] = pd_train.transferred.replace(np.NaN, 'None')
In [35]:
# Rechecking for Nulls, No Nulls now
In [36]:
pd_train_nan = (pd_train.isnull().sum() / pd_train.shape[0]) * 100
pd_train_nan
Out[36]:
date            0.0
store_nbr       0.0
transactions    0.0
unit_sales      0.0
dcoilwtico      0.0
type            0.0
locale          0.0
locale_name     0.0
description     0.0
transferred     0.0
dtype: float64

Generating Summary Statistics

In [37]:
# Inference
# 1089 Observations, 10 features
# Descriptive statistics reveal: average transaction volume is 4133 between 2013 - 2015 & Monthly average sales is $30586 for store 44 for the period 2013 - 2015
# Minimum transaction volume is 2333 & Maximum transaction volume is 8359.Min sales is $9067 and Max sales is $78070
# Oil price range from  $34 to $110
In [38]:
#Shape
print('Shape : ', pd_train.shape, '\n')

#Type
print('Type : ', '\n', pd_train.dtypes)

#Summary
pd_train.describe()
('Shape : ', (1089, 10), '\n')
('Type : ', '\n', date            datetime64[ns]
store_nbr                int64
transactions             int64
unit_sales             float64
dcoilwtico             float64
type                    object
locale                  object
locale_name             object
description             object
transferred             object
dtype: object)
Out[38]:
store_nbr transactions unit_sales dcoilwtico
count 1089.0 1089.000000 1089.000000 1089.000000
mean 44.0 4325.125803 32352.572317 79.983140
std 0.0 749.300106 13420.765145 24.087967
min 44.0 2333.000000 9067.748000 34.550000
25% 44.0 3797.000000 21326.734000 52.885000
50% 44.0 4133.000000 30586.076000 93.260000
75% 44.0 4787.000000 39148.231000 99.810000
max 44.0 8359.000000 78070.753000 110.620000
In [39]:
pd_train.sample(10)
Out[39]:
date store_nbr transactions unit_sales dcoilwtico type locale locale_name description transferred
1040 2014-05-14 44 4128 22226.754 102.630000 No_Holiday None None None None
652 2013-04-19 44 3558 18418.771 88.040000 No_Holiday None None None None
995 2014-03-30 44 5201 62628.939 101.623333 No_Holiday None None None None
548 2013-01-05 44 4921 31382.508 93.146667 Work Day National Ecuador Recupero puente Navidad False
67 2014-09-07 44 6099 75814.771 92.866667 No_Holiday None None None None
781 2013-08-26 44 3831 19270.846 105.880000 No_Holiday None None None None
699 2013-06-05 44 4107 21612.549 93.660000 No_Holiday None None None None
6 2014-07-08 44 3643 31809.408 104.060000 Event National Ecuador Mundial de futbol Brasil: Semifinales False
158 2014-12-07 44 5452 67381.384 64.050000 No_Holiday None None None None
877 2013-11-30 44 5181 32135.533 92.903333 No_Holiday None None None None
In [40]:
pd_train.type.unique()
Out[40]:
array(['No_Holiday', u'Event', u'Holiday', u'Additional', u'Transfer',
       u'Work Day', u'Bridge'], dtype=object)
In [41]:
pd_train.description.unique()
Out[41]:
array(['None', u'Mundial de futbol Brasil: Cuartos de Final',
       u'Mundial de futbol Brasil: Semifinales',
       u'Mundial de futbol Brasil: Tercer y cuarto lugar',
       u'Mundial de futbol Brasil: Final', u'Cantonizacion de Cayambe',
       u'Fundacion de Guayaquil-1', u'Fundacion de Guayaquil',
       u'Fundacion de Esmeraldas', u'Primer Grito de Independencia',
       u'Fundacion de Riobamba', u'Fundacion de Ambato',
       u'Fundacion de Ibarra', u'Cantonizacion de Quevedo',
       u'Independencia de Guayaquil',
       u'Traslado Independencia de Guayaquil', u'Dia de Difuntos',
       u'Independencia de Cuenca', u'Provincializacion de Santo Domingo',
       u'Provincializacion Santa Elena', u'Independencia de Guaranda',
       u'Independencia de Latacunga', u'Independencia de Ambato',
       u'Black Friday', u'Cyber Monday', u'Fundacion de Quito-1',
       u'Fundacion de Quito', u'Fundacion de Loja',
       u'Recupero Puente Navidad', u'Navidad-4', u'Navidad-3',
       u'Navidad-2', u'Navidad-1', u'Navidad+1', u'Primer dia del ano-1',
       u'Puente Primer dia del ano',
       u'Recupero Puente Primer dia del ano', u'Carnaval',
       u'Fundacion de Manta', u'Provincializacion de Cotopaxi',
       u'Viernes Santo', u'Fundacion de Cuenca',
       u'Cantonizacion de Libertad', u'Cantonizacion de Riobamba',
       u'Dia del Trabajo', u'Dia de la Madre-1', u'Dia de la Madre',
       u'Cantonizacion del Puyo', u'Batalla de Pichincha',
       u'Cantonizacion de Guaranda', u'Recupero puente Navidad',
       u'Recupero puente primer dia del ano',
       u'Inauguracion Mundial de futbol Brasil',
       u'Mundial de futbol Brasil: Ecuador-Suiza',
       u'Mundial de futbol Brasil: Ecuador-Honduras',
       u'Mundial de futbol Brasil: Ecuador-Francia',
       u'Mundial de futbol Brasil: Octavos de Final'], dtype=object)
In [42]:
pd_train.locale.unique()
Out[42]:
array(['None', u'National', u'Local', u'Regional'], dtype=object)
In [43]:
pd_train.transferred.unique()
Out[43]:
array(['None', False, True], dtype=object)

Data Pre-Processing

In [44]:
# Formatting date to YYYY-MM-DD
In [45]:
pd_train['date']=pd_train['date'].apply(lambda x: x.strftime('%Y-%m-%d'))
In [46]:
pd_train['date'].sample(10)
Out[46]:
737     2013-07-13
607     2013-03-05
912     2014-01-06
717     2013-06-23
925     2014-01-19
68      2014-09-08
619     2013-03-17
145     2014-11-24
836     2013-10-20
1060    2014-06-03
Name: date, dtype: object
In [47]:
pd_train.sample(10)
Out[47]:
date store_nbr transactions unit_sales dcoilwtico type locale locale_name description transferred
746 2013-07-22 44 3773 18184.387 106.610000 No_Holiday None None None None
1076 2014-06-19 44 3724 18725.544 107.080000 No_Holiday None None None None
1075 2014-06-18 44 4044 22035.203 106.640000 No_Holiday None None None None
747 2013-07-23 44 3576 17178.475 107.130000 Holiday Local Cayambe Cantonizacion de Cayambe False
481 2015-10-28 44 3978 33815.586 45.930000 No_Holiday None None None None
745 2013-07-21 44 4373 25648.429 107.073333 No_Holiday None None None None
622 2013-03-20 44 3806 19657.660 93.210000 No_Holiday None None None None
610 2013-03-08 44 3998 26132.241 92.010000 No_Holiday None None None None
5 2014-07-07 44 4013 37039.867 104.190000 No_Holiday None None None None
824 2013-10-08 44 3529 15810.702 103.540000 No_Holiday None None None None
In [48]:
# Reformat the date - Get Month Year
def get_month_year(df):
    df['month'] = df.date.apply(lambda x: x.split('-')[1])
    df['year'] = df.date.apply(lambda x: x.split('-')[0])
    
    return df

get_month_year(pd_train);
In [49]:
pd_train['date'] = pd.to_datetime(pd_train['date'])
pd_train['day'] = pd_train['date'].dt.weekday_name
pd_train = pd_train.drop('date', axis=1)
In [50]:
pd_train.sample(10)
Out[50]:
store_nbr transactions unit_sales dcoilwtico type locale locale_name description transferred month year day
1034 44 3769 16930.431 100.520000 No_Holiday None None None None 05 2014 Thursday
373 44 4669 50342.467 52.373333 No_Holiday None None None None 07 2015 Sunday
1017 44 3984 22809.098 104.350000 Holiday Local Riobamba Cantonizacion de Riobamba False 04 2014 Monday
42 44 4503 25680.017 97.570000 No_Holiday None None None None 08 2014 Wednesday
237 44 3548 16840.895 47.650000 No_Holiday None None None None 02 2015 Thursday
122 44 4843 52467.431 79.943333 No_Holiday None None None None 11 2014 Saturday
617 44 3732 18609.600 93.490000 No_Holiday None None None None 03 2013 Friday
692 44 3702 18342.311 93.130000 No_Holiday None None None None 05 2013 Wednesday
351 44 5857 63106.321 59.750000 No_Holiday None None None None 06 2015 Saturday
134 44 3714 29556.051 74.130000 No_Holiday None None None None 11 2014 Thursday
In [51]:
dummy_variables = ['type','store_nbr','locale', 'locale_name','transferred', 'month', 'day']

for var in dummy_variables:
    dummy = pd.get_dummies(pd_train[var], prefix = var, drop_first = False)
    pd_train = pd.concat([pd_train, dummy], axis = 1)

pd_train = pd_train.drop(dummy_variables, axis = 1)
pd_train = pd_train.drop(['year'], axis = 1)
In [52]:
pd_train=pd_train.drop("description",axis=1)
In [53]:
pd_train.sample(10)
Out[53]:
transactions unit_sales dcoilwtico type_Additional type_Bridge type_Event type_Holiday type_No_Holiday type_Transfer type_Work Day ... month_10 month_11 month_12 day_Friday day_Monday day_Saturday day_Sunday day_Thursday day_Tuesday day_Wednesday
326 3599 23840.864 57.290000 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 1 0
748 3882 18275.443 105.410000 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 1
311 4008 26314.777 59.230000 0 0 0 0 1 0 0 ... 0 0 0 0 1 0 0 0 0 0
893 5165 29312.315 97.180000 0 0 0 0 1 0 0 ... 0 0 1 0 1 0 0 0 0 0
272 4349 24349.005 49.130000 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 1 0 0
558 3527 15761.721 93.260000 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 1 0
318 3924 24386.601 59.440000 0 0 0 0 1 0 0 ... 0 0 0 0 1 0 0 0 0 0
240 5454 48394.660 49.673333 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 1 0 0 0
412 3693 28424.410 41.000000 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 1 0 0
343 4206 35997.924 59.960000 0 0 0 0 1 0 0 ... 0 0 0 1 0 0 0 0 0 0

10 rows × 57 columns

In [54]:
sns.regplot(x='dcoilwtico',
           y='transactions',
           data=pd_train,
           scatter_kws={'alpha':0.3},
           line_kws={'color':'black'})
Out[54]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74ea553410>
In [55]:
sns.regplot(x='dcoilwtico',
           y='unit_sales',
           data=pd_train,
           scatter_kws={'alpha':0.3},
           line_kws={'color':'black'})
Out[55]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74ea553e90>
In [56]:
#Re-scale Sales, Transactions, Oil Price using the standard scaler
scaler = preprocessing.StandardScaler()
pd_train['unit_sales'] = scaler.fit_transform(pd_train['unit_sales'].reshape(-1,1))
pd_train['dcoilwtico'] = scaler.fit_transform(pd_train['dcoilwtico'].reshape(-1,1))
pd_train['transactions'] = scaler.fit_transform(pd_train['transactions'].reshape(-1,1))
/usr/local/anaconda/python2/lib/python2.7/site-packages/ipykernel_launcher.py:3: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
  This is separate from the ipykernel package so we can avoid doing imports until
/usr/local/anaconda/python2/lib/python2.7/site-packages/ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
  after removing the cwd from sys.path.
/usr/local/anaconda/python2/lib/python2.7/site-packages/ipykernel_launcher.py:5: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
  """
/usr/local/anaconda/python2/lib/python2.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
  warnings.warn(msg, DataConversionWarning)
In [57]:
pd_train.sample(10)
Out[57]:
transactions unit_sales dcoilwtico type_Additional type_Bridge type_Event type_Holiday type_No_Holiday type_Transfer type_Work Day ... month_10 month_11 month_12 day_Friday day_Monday day_Saturday day_Sunday day_Thursday day_Tuesday day_Wednesday
138 -0.451463 0.155570 -0.180386 0 0 0 0 1 0 0 ... 0 1 0 0 1 0 0 0 0 0
269 -0.833328 -0.871900 -1.300962 0 0 0 0 1 0 0 ... 0 0 0 0 1 0 0 0 0 0
690 -1.161785 -1.189646 0.600757 0 0 0 0 1 0 0 ... 0 0 0 0 1 0 0 0 0 0
891 2.169519 0.280551 0.689050 0 0 0 0 1 0 0 ... 0 0 1 0 0 1 0 0 0 0
892 2.448574 0.726040 0.701649 0 0 0 0 1 0 0 ... 0 0 1 0 0 0 1 0 0 0
908 0.129346 1.093738 0.629519 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 1 0 0
344 0.846344 1.557465 -0.837586 0 0 0 0 1 0 0 ... 0 0 0 0 0 1 0 0 0 0
413 0.210792 0.426104 -1.641953 0 0 0 0 1 0 0 ... 0 0 0 1 0 0 0 0 0 0
948 -0.852020 -0.990681 0.829710 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 1 0
1004 -0.610351 -0.864920 0.938113 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 1 0

10 rows × 57 columns

In [58]:
print('Shape : ', pd_train.shape)
('Shape : ', (1089, 57))

Exploratory Data Analysis for store 44 with the prepared dataset

In [59]:
# The features highly correlated with transactions is Unit sales, Remove unit sales from the dependent variables.
# As the number of sales increases, total sales also increases and vice versa. Hence they are highly correlated and sales can be removed.
In [60]:
import matplotlib.pyplot as plt

corr = pd_train.corr()
corr.style.background_gradient()
/usr/local/anaconda/python2/lib/python2.7/site-packages/matplotlib/colors.py:489: RuntimeWarning: invalid value encountered in less
  np.copyto(xa, -1, where=xa < 0.0)
Out[60]:
transactions unit_sales dcoilwtico type_Additional type_Bridge type_Event type_Holiday type_No_Holiday type_Transfer type_Work Day store_nbr_44 locale_Local locale_National locale_None locale_Regional locale_name_Ambato locale_name_Cayambe locale_name_Cotopaxi locale_name_Cuenca locale_name_Ecuador locale_name_Esmeraldas locale_name_Guaranda locale_name_Guayaquil locale_name_Ibarra locale_name_Latacunga locale_name_Libertad locale_name_Loja locale_name_Manta locale_name_None locale_name_Puyo locale_name_Quevedo locale_name_Quito locale_name_Riobamba locale_name_Santa Elena locale_name_Santo Domingo de los Tsachilas transferred_False transferred_True transferred_None month_01 month_02 month_03 month_04 month_05 month_06 month_07 month_08 month_09 month_10 month_11 month_12 day_Friday day_Monday day_Saturday day_Sunday day_Thursday day_Tuesday day_Wednesday
transactions 1 0.669553 -0.16993 0.411036 0.0119767 -0.0189424 -0.00918325 -0.18198 -0.0381791 0.0876262 nan -0.0205949 0.268588 -0.18198 -0.0234987 -0.0174705 -0.0368981 -0.0109329 0.0360149 0.268588 -0.0247108 -0.0157479 -0.0283031 0.0279447 -0.0283132 -0.0184652 0.0313599 -0.00557613 -0.18198 -0.0326524 -0.0232137 0.0419264 0.00513877 -0.00384512 -0.0258103 0.185999 -0.0230879 -0.18198 -0.0688739 -0.0959127 -0.0392935 -0.0400283 -0.00831653 -0.0617048 -0.0997119 0.00371276 0.00389482 -0.0600355 0.00310667 0.463244 -0.0476949 -0.175411 0.430464 0.339924 -0.273889 -0.254552 -0.0197549
unit_sales 0.669553 1 -0.386863 0.105741 0.00210302 0.00982193 0.0438864 -0.0916298 -0.000893194 0.0237445 nan 0.0270568 0.0942718 -0.0916298 0.00988005 0.0246764 -0.0109058 -0.0253713 -0.00767745 0.0942718 -0.0156173 0.00764508 0.0045822 0.0484312 0.00234311 -0.0342451 0.0271941 0.00471135 -0.0916298 -0.0366385 0.00317551 0.0664237 -0.0117197 0.044606 -0.00216928 0.0949049 -0.0214675 -0.0916298 -0.0413347 -0.182907 -0.0330469 -0.164241 -0.146782 -0.0576254 0.0300072 -0.0331298 0.142057 0.110006 0.132059 0.24036 -0.104263 -0.100088 0.289987 0.35805 -0.253257 -0.168826 -0.0226208
dcoilwtico -0.16993 -0.386863 1 -0.067583 -0.034329 0.073501 -0.012225 0.013731 0.0250075 -0.0182953 nan -0.0120783 -0.00170463 0.013731 -0.0163785 -0.0147267 0.0138986 0.00506488 0.00504062 -0.00170463 0.00664388 -0.000396139 0.0178588 0.00146543 -0.0174778 0.00507701 -0.030658 0.00340341 0.013731 0.00146419 -9.41738e-05 -0.0395595 0.00803319 -0.0166046 -0.0167501 -0.0169187 0.0244285 0.013731 -0.0138751 0.0280465 0.00863445 0.034068 0.0686448 0.088491 0.0831976 0.027286 0.0211186 -0.0361736 -0.117972 -0.192871 0.00620741 -0.00131619 0.000590975 -0.000362609 0.00269112 -0.00210766 -0.00569448
type_Additional 0.411036 0.105741 -0.067583 1 -0.00474139 -0.0219303 -0.0469417 -0.40064 -0.00670842 -0.00949588 nan 0.0954311 0.452935 -0.40064 -0.0142768 -0.0116408 -0.00821989 -0.00821989 -0.00821989 0.452935 -0.00821989 -0.0116408 0.150882 -0.00821989 -0.00821989 -0.00821989 -0.00821989 -0.00821989 -0.40064 -0.00670842 -0.00821989 0.232144 -0.0116408 -0.00821989 -0.00821989 0.403879 -0.00670842 -0.40064 -0.0469417 -0.0452144 -0.0477894 -0.0469417 0.0167797 -0.0469417 -0.00474337 -0.0477894 -0.0469417 -0.0477894 -0.0469417 0.411823 -0.0292785 -0.01244 0.0390701 -0.0296101 0.0223699 0.00473004 0.00515377
type_Bridge 0.0119767 0.00210302 -0.034329 -0.00474139 1 -0.00425118 -0.00909964 -0.0776641 -0.00130043 -0.00184077 nan -0.00725593 0.110684 -0.0776641 -0.00276755 -0.00225656 -0.00159342 -0.00159342 -0.00159342 0.110684 -0.00159342 -0.00225656 -0.00225656 -0.00159342 -0.00159342 -0.00159342 -0.00159342 -0.00159342 -0.0776641 -0.00130043 -0.00159342 -0.00225656 -0.00225656 -0.00159342 -0.00159342 0.0782918 -0.00130043 -0.0776641 0.101006 -0.0087648 -0.00926397 -0.00909964 -0.00926397 -0.00909964 -0.00926397 -0.00926397 -0.00909964 -0.00926397 -0.00909964 -0.00909964 0.0744206 -0.0123967 -0.0123967 -0.0123967 -0.0123503 -0.0123967 -0.0123503
type_Event -0.0189424 0.00982193 0.073501 -0.0219303 -0.00425118 1 -0.0420884 -0.359218 -0.00601484 -0.00851411 nan -0.0335607 0.511943 -0.359218 -0.0128007 -0.0104372 -0.00737004 -0.00737004 -0.00737004 0.511943 -0.00737004 -0.0104372 -0.0104372 -0.00737004 -0.00737004 -0.00737004 -0.00737004 -0.00737004 -0.359218 -0.00601484 -0.00737004 -0.0104372 -0.0104372 -0.00737004 -0.00737004 0.362122 -0.00601484 -0.359218 -0.0420884 -0.0405397 -0.0428485 -0.0420884 0.028829 0.127668 0.124399 -0.0428485 -0.0420884 -0.0428485 0.0306644 -0.0178375 0.019322 -0.000157523 -0.000157523 0.0570233 -0.0380123 -0.0192178 -0.0189008
type_Holiday -0.00918325 0.0438864 -0.012225 -0.0469417 -0.00909964 -0.0420884 1 -0.768906 -0.0128747 -0.0182244 nan 0.723724 0.271195 -0.768906 0.304138 0.247984 0.175108 0.175108 0.175108 0.271195 0.175108 0.247984 0.157875 0.175108 0.175108 0.175108 0.175108 0.175108 -0.768906 0.14291 0.175108 0.112821 0.247984 0.175108 0.175108 0.755313 0.14291 -0.768906 -0.0900901 -0.0367761 -0.0320517 0.0915916 0.00374758 -0.0537538 -0.0081855 0.0514799 -0.0537538 -0.0201186 0.164264 -0.0174174 -0.00773082 0.0676603 -0.0180165 0.0105424 -0.0459116 0.0295817 -0.0363664
type_No_Holiday -0.18198 -0.0916298 0.013731 -0.40064 -0.0776641 -0.359218 -0.768906 1 -0.109884 -0.155543 nan -0.613115 -0.701676 1 -0.233854 -0.190676 -0.134642 -0.134642 -0.134642 -0.701676 -0.134642 -0.190676 -0.190676 -0.134642 -0.134642 -0.134642 -0.134642 -0.134642 1 -0.109884 -0.134642 -0.190676 -0.190676 -0.134642 -0.134642 -0.991982 -0.109884 1 0.0777856 0.072214 0.070784 -0.0305121 -0.0165136 0.0187141 -0.035913 0.00288587 0.0876309 0.0416848 -0.119119 -0.168346 -0.00391143 -0.0415677 -0.0260917 -0.0106157 0.0503998 -0.0106157 0.042641
type_Transfer -0.0381791 -0.000893194 0.0250075 -0.00670842 -0.00130043 -0.00601484 -0.0128747 -0.109884 1 -0.00260445 nan -0.0102662 0.156602 -0.109884 -0.0039157 -0.00319273 -0.00225448 -0.00225448 -0.00225448 0.156602 -0.00225448 -0.00319273 -0.00319273 -0.00225448 -0.00225448 -0.00225448 -0.00225448 -0.00225448 -0.109884 -0.00183993 -0.00225448 -0.00319273 -0.00319273 -0.00225448 -0.00225448 0.110772 -0.00183993 -0.109884 -0.0128747 -0.012401 -0.0131073 -0.0128747 -0.0131073 -0.0128747 -0.0131073 -0.0131073 -0.0128747 0.140375 -0.0128747 -0.0128747 0.105295 -0.0175397 -0.0175397 -0.0175397 -0.017474 -0.0175397 -0.017474
type_Work Day 0.0876262 0.0237445 -0.0182953 -0.00949588 -0.00184077 -0.00851411 -0.0182244 -0.155543 -0.00260445 1 nan -0.0145319 0.221673 -0.155543 -0.00554274 -0.00451936 -0.00319125 -0.00319125 -0.00319125 0.221673 -0.00319125 -0.00451936 -0.00451936 -0.00319125 -0.00319125 -0.00319125 -0.00319125 -0.00319125 -0.155543 -0.00260445 -0.00319125 -0.00451936 -0.00451936 -0.00319125 -0.00319125 0.1568 -0.00260445 -0.155543 0.147162 -0.0175538 -0.0185535 -0.0182244 -0.0185535 -0.0182244 -0.0185535 -0.0185535 -0.0182244 -0.0185535 -0.0182244 0.0369044 -0.0247348 -0.0248277 0.148489 -0.0248277 -0.0247348 -0.0248277 -0.0247348
store_nbr_44 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
locale_Local -0.0205949 0.0270568 -0.0120783 0.0954311 -0.00725593 -0.0335607 0.723724 -0.613115 -0.0102662 -0.0145319 nan 1 -0.0655556 -0.613115 -0.0218483 0.310995 0.219603 -0.0125792 0.219603 -0.0655556 0.219603 0.310995 0.310995 0.219603 0.219603 0.219603 0.219603 0.219603 -0.613115 0.179223 0.219603 0.310995 0.310995 -0.0125792 -0.0125792 0.618071 -0.0102662 -0.613115 -0.0718366 -0.0691933 -0.0295895 0.0607567 -0.0441043 -0.0276388 0.0574995 0.0574995 -0.0276388 -0.0295895 0.0607567 0.0607567 -0.027837 0.0410856 -0.00523139 0.0179271 -0.027837 0.0295063 -0.027837
locale_National 0.268588 0.0942718 -0.00170463 0.452935 0.110684 0.511943 0.271195 -0.701676 0.156602 0.221673 nan -0.0655556 1 -0.701676 -0.0250041 -0.0203875 -0.0143962 -0.0143962 -0.0143962 1 -0.0143962 -0.0203875 -0.0203875 -0.0143962 -0.0143962 -0.0143962 -0.0143962 -0.0143962 -0.701676 -0.011749 -0.0143962 -0.0203875 -0.0203875 -0.0143962 -0.0143962 0.685942 0.156602 -0.701676 -0.0298563 -0.0251555 -0.0579064 -0.0429455 0.0710507 0.00941123 0.00657219 -0.0450106 -0.082213 -0.0192192 0.0355896 0.179571 0.0225153 0.0217371 0.0423123 0.0114495 -0.049691 -0.00912562 -0.0393758
locale_None -0.18198 -0.0916298 0.013731 -0.40064 -0.0776641 -0.359218 -0.768906 1 -0.109884 -0.155543 nan -0.613115 -0.701676 1 -0.233854 -0.190676 -0.134642 -0.134642 -0.134642 -0.701676 -0.134642 -0.190676 -0.190676 -0.134642 -0.134642 -0.134642 -0.134642 -0.134642 1 -0.109884 -0.134642 -0.190676 -0.190676 -0.134642 -0.134642 -0.991982 -0.109884 1 0.0777856 0.072214 0.070784 -0.0305121 -0.0165136 0.0187141 -0.035913 0.00288587 0.0876309 0.0416848 -0.119119 -0.168346 -0.00391143 -0.0415677 -0.0260917 -0.0106157 0.0503998 -0.0106157 0.042641
locale_Regional -0.0234987 0.00988005 -0.0163785 -0.0142768 -0.00276755 -0.0128007 0.304138 -0.233854 -0.0039157 -0.00554274 nan -0.0218483 -0.0250041 -0.233854 1 -0.00679471 -0.00479794 0.575753 -0.00479794 -0.0250041 -0.00479794 -0.00679471 -0.00679471 -0.00479794 -0.00479794 -0.00479794 -0.00479794 -0.00479794 -0.233854 -0.0039157 -0.00479794 -0.00679471 -0.00679471 0.575753 0.575753 0.235744 -0.0039157 -0.233854 -0.0273998 -0.0263916 -0.0278947 0.0831128 -0.0278947 -0.0273998 -0.0278947 -0.0278947 -0.0273998 -0.0278947 0.193625 -0.0273998 0.0208732 -0.0083748 -0.0083748 -0.0373277 0.0208732 -0.0083748 0.0208732
locale_name_Ambato -0.0174705 0.0246764 -0.0147267 -0.0116408 -0.00225656 -0.0104372 0.247984 -0.190676 -0.00319273 -0.00451936 nan 0.310995 -0.0203875 -0.190676 -0.00679471 1 -0.00391207 -0.00391207 -0.00391207 -0.0203875 -0.00391207 -0.00554017 -0.00554017 -0.00391207 -0.00391207 -0.00391207 -0.00391207 -0.00391207 -0.190676 -0.00319273 -0.00391207 -0.00554017 -0.00554017 -0.00391207 -0.00391207 0.192217 -0.00319273 -0.190676 -0.0223409 -0.0215188 -0.0227443 -0.0223409 -0.0227443 -0.0223409 -0.0227443 0.11042 -0.0223409 -0.0227443 0.112821 -0.0223409 -0.0303217 0.00497506 0.00497506 0.00497506 0.00518404 0.00497506 0.00518404
locale_name_Cayambe -0.0368981 -0.0109058 0.0138986 -0.00821989 -0.00159342 -0.00737004 0.175108 -0.134642 -0.00225448 -0.00319125 nan 0.219603 -0.0143962 -0.134642 -0.00479794 -0.00391207 1 -0.00276243 -0.00276243 -0.0143962 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.134642 -0.00225448 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 0.13573 -0.00225448 -0.134642 -0.0157755 -0.0151951 -0.0160604 -0.0157755 -0.0160604 -0.0157755 0.172002 -0.0160604 -0.0157755 -0.0160604 -0.0157755 -0.0157755 -0.0214111 -0.0214915 -0.0214915 -0.0214915 0.0287323 0.0285176 0.0287323
locale_name_Cotopaxi -0.0109329 -0.0253713 0.00506488 -0.00821989 -0.00159342 -0.00737004 0.175108 -0.134642 -0.00225448 -0.00319125 nan -0.0125792 -0.0143962 -0.134642 0.575753 -0.00391207 -0.00276243 1 -0.00276243 -0.0143962 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.134642 -0.00225448 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 0.13573 -0.00225448 -0.134642 -0.0157755 -0.0151951 -0.0160604 0.175108 -0.0160604 -0.0157755 -0.0160604 -0.0160604 -0.0157755 -0.0160604 -0.0157755 -0.0157755 -0.0214111 0.0285176 -0.0214915 -0.0214915 -0.0214111 0.0285176 0.0287323
locale_name_Cuenca 0.0360149 -0.00767745 0.00504062 -0.00821989 -0.00159342 -0.00737004 0.175108 -0.134642 -0.00225448 -0.00319125 nan 0.219603 -0.0143962 -0.134642 -0.00479794 -0.00391207 -0.00276243 -0.00276243 1 -0.0143962 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.134642 -0.00225448 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 0.13573 -0.00225448 -0.134642 -0.0157755 -0.0151951 -0.0160604 0.175108 -0.0160604 -0.0157755 -0.0160604 -0.0160604 -0.0157755 -0.0160604 -0.0157755 -0.0157755 0.0287323 -0.0214915 0.0285176 0.0285176 -0.0214111 -0.0214915 -0.0214111
locale_name_Ecuador 0.268588 0.0942718 -0.00170463 0.452935 0.110684 0.511943 0.271195 -0.701676 0.156602 0.221673 nan -0.0655556 1 -0.701676 -0.0250041 -0.0203875 -0.0143962 -0.0143962 -0.0143962 1 -0.0143962 -0.0203875 -0.0203875 -0.0143962 -0.0143962 -0.0143962 -0.0143962 -0.0143962 -0.701676 -0.011749 -0.0143962 -0.0203875 -0.0203875 -0.0143962 -0.0143962 0.685942 0.156602 -0.701676 -0.0298563 -0.0251555 -0.0579064 -0.0429455 0.0710507 0.00941123 0.00657219 -0.0450106 -0.082213 -0.0192192 0.0355896 0.179571 0.0225153 0.0217371 0.0423123 0.0114495 -0.049691 -0.00912562 -0.0393758
locale_name_Esmeraldas -0.0247108 -0.0156173 0.00664388 -0.00821989 -0.00159342 -0.00737004 0.175108 -0.134642 -0.00225448 -0.00319125 nan 0.219603 -0.0143962 -0.134642 -0.00479794 -0.00391207 -0.00276243 -0.00276243 -0.00276243 -0.0143962 1 -0.00391207 -0.00391207 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.00276243 -0.134642 -0.00225448 -0.00276243 -0.00391207 -0.00391207 -0.00276243 -0.00276243 0.13573 -0.00225448 -0.134642 -0.0157755 -0.0151951 -0.0160604 -0.0157755 -0.0160604 -0.0157755 -0.0160604 0.172002 -0.0157755 -0.0160604 -0.0157755 -0.0157755 -0.0214111 0.0285176 -0.0214915 -0.0214915 -0.0214111 0.0285176 0.0287323
locale_name_Guaranda -0.0157479 0.00764508 -0.000396139 -0.0116408 -0.00225656 -0.0104372 0.247984 -0.190676 -0.00319273 -0.00451936 nan 0.310995 -0.0203875 -0.190676 -0.00679471 -0.00554017 -0.00391207 -0.00391207 -0.00391207 -0.0203875 -0.00391207 1 -0.00554017 -0.00391207 -0.00391207 -0.00391207 -0.00391207 -0.00391207 -0.190676 -0.00319273 -0.00391207 -0.00554017 -0.00554017 -0.00391207 -0.00391207 0.192217 -0.00319273 -0.190676 -0.0223409 -0.0215188 -0.0227443 -0.0223409 -0.0227443 0.112821 -0.0227443 -0.0227443 -0.0223409 -0.0227443 0.112821 -0.0223409 -0.0303217 0.0403858 -0.0304357 0.0403858 -0.0303217 0.0403858 -0.0303217
locale_name_Guayaquil -0.0283031 0.0045822 0.0178588 0.150882 -0.00225656 -0.0104372 0.157875 -0.190676 -0.00319273 -0.00451936 nan 0.310995 -0.0203875 -0.190676 -0.00679471 -0.00554017 -0.00391207 -0.00391207 -0.00391207 -0.0203875 -0.00391207 -0.00554017 1 -0.00391207 -0.00391207 -0.00391207 -0.00391207 -0.00391207 -0.190676 -0.00319273 -0.00391207 -0.00554017 -0.00554017 -0.00391207 -0.00391207 0.192217 -0.00319273 -0.190676 -0.0223409 -0.0215188 -0.0227443 -0.0223409 -0.0227443 -0.0223409 0.243584 -0.0227443 -0.0223409 -0.0227443 -0.0223409 -0.0223409 0.0406898 -0.0304357 0.00497506 -0.0304357 0.0406898 -0.0304357 0.00518404
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month_11 0.00310667 0.132059 -0.117972 -0.0469417 -0.00909964 0.0306644 0.164264 -0.119119 -0.0128747 -0.0182244 nan 0.0607567 0.0355896 -0.119119 0.193625 0.112821 -0.0157755 -0.0157755 -0.0157755 0.0355896 -0.0157755 0.112821 -0.0223409 -0.0157755 0.175108 -0.0157755 -0.0157755 -0.0157755 -0.119119 -0.0128747 -0.0157755 -0.0223409 -0.0223409 0.175108 0.175108 0.121466 -0.0128747 -0.119119 -0.0900901 -0.0867751 -0.0917171 -0.0900901 -0.0917171 -0.0900901 -0.0917171 -0.0917171 -0.0900901 -0.0917171 1 -0.0900901 0.00181438 0.00102277 0.0105424 0.0105424 -0.00773082 -0.00849687 -0.00773082
month_12 0.463244 0.24036 -0.192871 0.411823 -0.00909964 -0.0178375 -0.0174174 -0.168346 -0.0128747 0.0369044 nan 0.0607567 0.179571 -0.168346 -0.0273998 -0.0223409 -0.0157755 -0.0157755 -0.0157755 0.179571 -0.0157755 -0.0223409 -0.0223409 -0.0157755 -0.0157755 -0.0157755 0.175108 -0.0157755 -0.168346 -0.0128747 -0.0157755 0.247984 -0.0223409 -0.0157755 -0.0157755 0.170985 -0.0128747 -0.168346 -0.0900901 -0.0867751 -0.0917171 -0.0900901 -0.0917171 -0.0900901 -0.0917171 -0.0917171 -0.0900901 -0.0917171 -0.0900901 1 -0.017276 0.0105424 -0.00849687 0.00102277 -0.00773082 0.0200621 0.00181438
day_Friday -0.0476949 -0.104263 0.00620741 -0.0292785 0.0744206 0.019322 -0.00773082 -0.00391143 0.105295 -0.0247348 nan -0.027837 0.0225153 -0.00391143 0.0208732 -0.0303217 -0.0214111 -0.0214111 0.0287323 0.0225153 -0.0214111 -0.0303217 0.0406898 -0.0214111 -0.0214111 -0.0214111 -0.0214111 -0.0214111 -0.00391143 -0.017474 -0.0214111 0.0406898 0.00518404 0.0287323 0.0287323 0.00615651 -0.017474 -0.00391143 0.0113596 0.000434189 -0.00222797 -0.00773082 0.0165802 -0.00773082 -0.00222797 0.00717613 -0.00773082 0.00717613 0.00181438 -0.017276 1 -0.166577 -0.166577 -0.166577 -0.165953 -0.166577 -0.165953
day_Monday -0.175411 -0.100088 -0.00131619 -0.01244 -0.0123967 -0.000157523 0.0676603 -0.0415677 -0.0175397 -0.0248277 nan 0.0410856 0.0217371 -0.0415677 -0.0083748 0.00497506 -0.0214915 0.0285176 -0.0214915 0.0217371 0.0285176 0.0403858 -0.0304357 0.0285176 0.0285176 0.0285176 0.0285176 0.0285176 -0.0415677 0.0436806 0.0285176 -0.0304357 0.00497506 -0.0214915 -0.0214915 0.0440453 -0.0175397 -0.0415677 -0.00849687 -0.000324771 0.00635597 0.00102277 -0.0124019 0.0105424 -0.00302296 -0.00302296 0.0105424 -0.0124019 0.00102277 0.0105424 -0.166577 1 -0.167203 -0.167203 -0.166577 -0.167203 -0.166577
day_Saturday 0.430464 0.289987 0.000590975 0.0390701 -0.0123967 -0.000157523 -0.0180165 -0.0260917 -0.0175397 0.148489 nan -0.00523139 0.0423123 -0.0260917 -0.0083748 0.00497506 -0.0214915 -0.0214915 0.0285176 0.0423123 -0.0214915 -0.0304357 0.00497506 0.0285176 -0.0214915 -0.0214915 -0.0214915 0.0285176 -0.0260917 -0.0175397 -0.0214915 0.0403858 0.00497506 0.0285176 -0.0214915 0.0284772 -0.0175397 -0.0260917 0.00102277 -0.000324771 0.00635597 -0.00849687 0.00635597 0.00102277 -0.0124019 0.0157349 -0.00849687 -0.00302296 0.0105424 -0.00849687 -0.166577 -0.167203 1 -0.167203 -0.166577 -0.167203 -0.166577
day_Sunday 0.339924 0.35805 -0.000362609 -0.0296101 -0.0123967 0.0570233 0.0105424 -0.0106157 -0.0175397 -0.0248277 nan 0.0179271 0.0114495 -0.0106157 -0.0373277 0.00497506 -0.0214915 -0.0214915 0.0285176 0.0114495 -0.0214915 0.0403858 -0.0304357 0.0285176 -0.0214915 0.0285176 0.0285176 0.0285176 -0.0106157 -0.0175397 -0.0214915 0.00497506 0.00497506 -0.0214915 -0.0214915 0.0129091 -0.0175397 -0.0106157 -0.00849687 -0.000324771 0.0157349 -0.00849687 -0.00302296 0.0105424 -0.0124019 0.00635597 0.00102277 -0.0124019 0.0105424 0.00102277 -0.166577 -0.167203 -0.167203 1 -0.166577 -0.167203 -0.166577
day_Thursday -0.273889 -0.253257 0.00269112 0.0223699 -0.0123503 -0.0380123 -0.0459116 0.0503998 -0.017474 -0.0247348 nan -0.027837 -0.049691 0.0503998 0.0208732 0.00518404 0.0287323 -0.0214111 -0.0214111 -0.049691 -0.0214111 -0.0303217 0.0406898 -0.0214111 -0.0214111 -0.0214111 -0.0214111 -0.0214111 0.0503998 -0.017474 -0.0214111 0.00518404 0.00518404 0.0287323 0.0287323 -0.056283 0.0439105 0.0503998 0.0113596 0.000434189 -0.0116321 0.00181438 0.00717613 -0.00773082 0.00717613 -0.00222797 -0.00773082 0.0165802 -0.00773082 -0.00773082 -0.165953 -0.166577 -0.166577 -0.166577 1 -0.166577 -0.165953
day_Tuesday -0.254552 -0.168826 -0.00210766 0.00473004 -0.0123967 -0.0192178 0.0295817 -0.0106157 -0.0175397 -0.0248277 nan 0.0295063 -0.00912562 -0.0106157 -0.0083748 0.00497506 0.0285176 0.0285176 -0.0214915 -0.00912562 0.0285176 0.0403858 -0.0304357 -0.0214915 0.0285176 0.0285176 0.0285176 -0.0214915 -0.0106157 0.0436806 0.0285176 -0.0304357 0.00497506 -0.0214915 -0.0214915 0.0129091 -0.0175397 -0.0106157 -0.00849687 -0.000324771 -0.00302296 0.0105424 -0.0124019 0.00102277 0.00635597 -0.0124019 0.0105424 -0.00302296 -0.00849687 0.0200621 -0.166577 -0.167203 -0.167203 -0.167203 -0.166577 1 -0.166577
day_Wednesday -0.0197549 -0.0226208 -0.00569448 0.00515377 -0.0123503 -0.0189008 -0.0363664 0.042641 -0.017474 -0.0247348 nan -0.027837 -0.0393758 0.042641 0.0208732 0.00518404 0.0287323 0.0287323 -0.0214111 -0.0393758 0.0287323 -0.0303217 0.00518404 -0.0214111 0.0287323 -0.0214111 -0.0214111 -0.0214111 0.042641 -0.017474 0.0287323 -0.0303217 -0.0303217 -0.0214111 0.0287323 -0.0484781 0.0439105 0.042641 0.00181438 0.000434189 -0.0116321 0.0113596 -0.00222797 -0.00773082 0.0165802 -0.0116321 0.00181438 0.00717613 -0.00773082 0.00181438 -0.165953 -0.166577 -0.166577 -0.166577 -0.165953 -0.166577 1
In [61]:
plt.figure(figsize=(32,32))
plt.matshow(pd_train.corr(), cmap=plt.cm.Reds, fignum=1)
plt.colorbar()
tick_marks = [i for i in range(len(pd_train.columns))]
plt.xticks(tick_marks, pd_train.columns, rotation=90)
plt.yticks(tick_marks, pd_train.columns)
Out[61]:
([<matplotlib.axis.YTick at 0x7f74daf90050>,
  <matplotlib.axis.YTick at 0x7f74ea553fd0>,
  <matplotlib.axis.YTick at 0x7f74daf61a90>,
  <matplotlib.axis.YTick at 0x7f74daf61f90>,
  <matplotlib.axis.YTick at 0x7f74daf6b510>,
  <matplotlib.axis.YTick at 0x7f74daf4ea90>,
  <matplotlib.axis.YTick at 0x7f74daf6bc10>,
  <matplotlib.axis.YTick at 0x7f74daf76190>,
  <matplotlib.axis.YTick at 0x7f74daf766d0>,
  <matplotlib.axis.YTick at 0x7f74daf76c10>,
  <matplotlib.axis.YTick at 0x7f74daebf190>,
  <matplotlib.axis.YTick at 0x7f74daebf6d0>,
  <matplotlib.axis.YTick at 0x7f74daebfc10>,
  <matplotlib.axis.YTick at 0x7f74daecd190>,
  <matplotlib.axis.YTick at 0x7f74daecd6d0>,
  <matplotlib.axis.YTick at 0x7f74daecdc10>,
  <matplotlib.axis.YTick at 0x7f74daed7190>,
  <matplotlib.axis.YTick at 0x7f74daed76d0>,
  <matplotlib.axis.YTick at 0x7f74daed7c10>,
  <matplotlib.axis.YTick at 0x7f74daee2190>,
  <matplotlib.axis.YTick at 0x7f74daee26d0>,
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  <matplotlib.axis.YTick at 0x7f74dae8d6d0>,
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  <matplotlib.axis.YTick at 0x7f74daeaf690>],
 <a list of 57 Text yticklabel objects>)
In [62]:
# Keeping only the highly correlated features for plotting the distribution
pd_train_filtered = pd_train[['transactions','dcoilwtico','unit_sales','type_Additional','month_12','day_Saturday','day_Sunday']]
In [63]:
# Check the data distribution through pairplot
sns.pairplot(pd_train_filtered)
Out[63]:
<seaborn.axisgrid.PairGrid at 0x7f74daff7c90>
In [64]:
sns.regplot(x='dcoilwtico',
           y='transactions',
           data=pd_train,
           scatter_kws={'alpha':0.3},
           line_kws={'color':'black'})
Out[64]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74d99a9950>
In [65]:
sns.regplot(x='dcoilwtico',
           y='unit_sales',
           data=pd_train,
           scatter_kws={'alpha':0.3},
           line_kws={'color':'black'})
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74d9b2ff50>
In [66]:
sns.regplot(x='transactions',
           y='unit_sales',
           data=pd_train,
           scatter_kws={'alpha':0.3},
           line_kws={'color':'black'})
Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74d98f2ad0>

Checking for statistical significance

Is there any statistically significant relation between Oil price and Transaction Volume for the store 44 ?

Null Hypothesis H0 = Oil price and Transaction Volume are independent from each other.

Alternative Hypothesis HA = Oil price and Transaction Volume are not independent of each other. There is a relationship between them.

Oil Price - Independent continuous variable

Transaction Volume - Dependent continuous variable

In [67]:
lin_model = smf.ols(formula = 'transactions ~ dcoilwtico', data = pd_train).fit()
In [68]:
#print the summary 
print(lin_model.summary())
                            OLS Regression Results                            
==============================================================================
Dep. Variable:           transactions   R-squared:                       0.029
Model:                            OLS   Adj. R-squared:                  0.028
Method:                 Least Squares   F-statistic:                     32.32
Date:                Wed, 08 Aug 2018   Prob (F-statistic):           1.68e-08
Time:                        06:51:27   Log-Likelihood:                -1529.3
No. Observations:                1089   AIC:                             3063.
Df Residuals:                    1087   BIC:                             3073.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept  -1.108e-16      0.030  -3.71e-15      1.000      -0.059       0.059
dcoilwtico    -0.1699      0.030     -5.685      0.000      -0.229      -0.111
==============================================================================
Omnibus:                      283.334   Durbin-Watson:                   0.899
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              801.415
Skew:                           1.315   Prob(JB):                    9.44e-175
Kurtosis:                       6.277   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
In [69]:
# Inference
# p-value is < 0.05 and hence there is relationship between transaction volume and oil price. 
# So, rejecting the null hypothesis

Is there any statistically significant relation between Oil price and Sales for the store 44 ?

Null Hypothesis H0 = Oil price and sales are independent from each other.

Alternative Hypothesis HA = Oil price and sales are not independent of each other. There is a relationship between them.

Oil Price - Independent continuous variable

Sales - Dependent continuous variable

In [70]:
lin_model = smf.ols(formula = 'unit_sales ~ dcoilwtico', data = pd_train).fit()
In [71]:
#print the summary 
print(lin_model.summary())
                            OLS Regression Results                            
==============================================================================
Dep. Variable:             unit_sales   R-squared:                       0.150
Model:                            OLS   Adj. R-squared:                  0.149
Method:                 Least Squares   F-statistic:                     191.3
Date:                Wed, 08 Aug 2018   Prob (F-statistic):           3.36e-40
Time:                        06:51:29   Log-Likelihood:                -1456.9
No. Observations:                1089   AIC:                             2918.
Df Residuals:                    1087   BIC:                             2928.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept  -1.716e-16      0.028  -6.14e-15      1.000      -0.055       0.055
dcoilwtico    -0.3869      0.028    -13.832      0.000      -0.442      -0.332
==============================================================================
Omnibus:                      131.140   Durbin-Watson:                   0.788
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              179.948
Skew:                           0.923   Prob(JB):                     8.41e-40
Kurtosis:                       3.747   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
In [72]:
# Inference
# p-value is < 0.05 and hence there is a relationship between oil price and sales. 
# So, rejecting the null hypothesis

Train Test split

In [73]:
# Modeling for Transactions as the dependent variable and all the other variables from the filtered dataframe except total sales as the independent variables.
In [74]:
X_train = pd_train.drop(['unit_sales','transactions'], axis = 1)
y_labels = pd_train['transactions']
In [75]:
num_test = 0.35
X_train, X_test, y_train, Y_test = train_test_split(X_train, y_labels, test_size=num_test, random_state=15)
print('X_train shape :', X_train.shape)
print('y_train shape :', y_train.shape)
print('X_test shape :', X_test.shape)
print('y_test shape :', Y_test.shape)
('X_train shape :', (707, 55))
('y_train shape :', (707,))
('X_test shape :', (382, 55))
('y_test shape :', (382,))

Transaction Volume Prediction

Model1a: Linear regression using the direct closed-form normal equation

invoke the LinearRegression function and find the bestfit model on training data

In [76]:
regression_model = LinearRegression()
regression_model.fit(X_train.as_matrix(), y_train)
Out[76]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
In [77]:
print(regression_model.coef_)
print(regression_model.intercept_)
[-9.60034593e-02 -5.49010914e+11 -5.49010914e+11 -5.49010914e+11
 -5.49010914e+11  2.47462746e+12 -5.49010914e+11 -5.49010914e+11
 -2.18988857e+11  6.64353677e+11 -3.81721180e+11 -7.06510539e+11
  1.70336126e+11 -2.44556192e+11 -2.44556192e+11  2.49461359e+11
 -2.44556192e+11  8.01518665e+11 -2.44556192e+11 -2.44556192e+11
 -2.44556192e+11 -2.44556192e+11 -2.44556192e+11 -2.44556192e+11
 -2.44556192e+11 -2.44556192e+11 -5.90112330e+11 -2.44556192e+11
 -2.44556192e+11 -2.44556192e+11 -2.44556192e+11  2.49461359e+11
  2.49461359e+11  7.18331547e+11  7.18331547e+11 -5.88886473e+11
  5.69445055e+10  5.69445055e+10  5.69445055e+10  5.69445055e+10
  5.69445055e+10  5.69445055e+10  5.69445055e+10  5.69445055e+10
  5.69445055e+10  5.69445055e+10  5.69445055e+10  5.69445055e+10
  8.42902881e+11  8.42902881e+11  8.42902881e+11  8.42902881e+11
  8.42902881e+11  8.42902881e+11  8.42902881e+11]
-1269976647537.2515
In [78]:
# Checking the coefficients for each of the independent attributes

for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, regression_model.coef_[idx]))
The coefficient for dcoilwtico is -0.0960034593203
The coefficient for type_Additional is -5.49010913821e+11
The coefficient for type_Bridge is -5.49010913823e+11
The coefficient for type_Event is -5.49010913824e+11
The coefficient for type_Holiday is -5.49010913823e+11
The coefficient for type_No_Holiday is 2.47462745863e+12
The coefficient for type_Transfer is -5.49010913826e+11
The coefficient for type_Work Day is -5.49010913823e+11
The coefficient for store_nbr_44 is -2.18988856656e+11
The coefficient for locale_Local is 6.64353677112e+11
The coefficient for locale_National is -3.81721179938e+11
The coefficient for locale_None is -7.065105386e+11
The coefficient for locale_Regional is 1.70336126173e+11
The coefficient for locale_name_Ambato is -2.44556191929e+11
The coefficient for locale_name_Cayambe is -2.44556191928e+11
The coefficient for locale_name_Cotopaxi is 2.49461359012e+11
The coefficient for locale_name_Cuenca is -2.44556191928e+11
The coefficient for locale_name_Ecuador is 8.01518665122e+11
The coefficient for locale_name_Esmeraldas is -2.44556191928e+11
The coefficient for locale_name_Guaranda is -2.44556191928e+11
The coefficient for locale_name_Guayaquil is -2.44556191929e+11
The coefficient for locale_name_Ibarra is -2.44556191928e+11
The coefficient for locale_name_Latacunga is -2.44556191929e+11
The coefficient for locale_name_Libertad is -2.44556191929e+11
The coefficient for locale_name_Loja is -2.44556191929e+11
The coefficient for locale_name_Manta is -2.44556191929e+11
The coefficient for locale_name_None is -5.90112329592e+11
The coefficient for locale_name_Puyo is -2.44556191929e+11
The coefficient for locale_name_Quevedo is -2.44556191928e+11
The coefficient for locale_name_Quito is -2.4455619193e+11
The coefficient for locale_name_Riobamba is -2.44556191928e+11
The coefficient for locale_name_Santa Elena is 2.49461359011e+11
The coefficient for locale_name_Santo Domingo de los Tsachilas is 2.49461359011e+11
The coefficient for transferred_False is 7.18331546548e+11
The coefficient for transferred_True is 7.18331546547e+11
The coefficient for transferred_None is -5.88886472526e+11
The coefficient for month_01 is 56944505471.7
The coefficient for month_02 is 56944505471.7
The coefficient for month_03 is 56944505471.9
The coefficient for month_04 is 56944505471.9
The coefficient for month_05 is 56944505471.9
The coefficient for month_06 is 56944505471.9
The coefficient for month_07 is 56944505471.8
The coefficient for month_08 is 56944505472.0
The coefficient for month_09 is 56944505472.0
The coefficient for month_10 is 56944505471.8
The coefficient for month_11 is 56944505471.9
The coefficient for month_12 is 56944505473.2
The coefficient for day_Friday is 8.42902880812e+11
The coefficient for day_Monday is 8.42902880811e+11
The coefficient for day_Saturday is 8.42902880813e+11
The coefficient for day_Sunday is 8.42902880813e+11
The coefficient for day_Thursday is 8.42902880811e+11
The coefficient for day_Tuesday is 8.42902880811e+11
The coefficient for day_Wednesday is 8.42902880812e+11
In [79]:
# Let us check the intercept for the model

intercept = regression_model.intercept_

print("The intercept for the model is {}".format(intercept))
The intercept for the model is -1.26997664754e+12
In [80]:
regression_model.score(X_test, Y_test)
Out[80]:
0.6558644803692054
In [81]:
# checking the sum of squared errors by predicting value of y for test cases and 
# subtracting from the actual y for the test cases

mse = np.mean((regression_model.predict(X_test)-Y_test)**2)
In [82]:
# underroot of mean_sq_error is standard deviation i.e. avg variance between predicted and actual

import math

math.sqrt(mse)
Out[82]:
0.5755537381618258
In [83]:
# predict transaction volume for a set of attributes 
y_pred = regression_model.predict(X_test)
In [84]:
# Since this is regression, plot the predicted y value vs actual y values for the test data
plt.scatter(Y_test, y_pred)
Out[84]:
<matplotlib.collections.PathCollection at 0x7f74d9823390>
In [85]:
from sklearn.metrics import r2_score
R2_Lin_Reg_Tran_Pred = r2_score(Y_test, y_pred)
print(R2_Lin_Reg_Tran_Pred)
0.6558644803692054
In [86]:
y_train_pred = regression_model.predict(X_train)

# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Train data
print(mean_absolute_error(y_train, y_train_pred))
print(mean_squared_error(y_train, y_train_pred))
print(np.sqrt(mean_squared_error(y_train, y_train_pred)))
0.34761541123205175
0.24531856745132752
0.49529644401239903
In [87]:
RMSE_Lin_Reg_Tran_Pred = np.sqrt(mean_squared_error(Y_test, y_pred))
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of simple linear regression model is : " + str(mean_absolute_error(Y_test, y_pred)))
print("The mean squared error of simple linear regression model is : " + str(mean_squared_error(Y_test, y_pred)))
print("The root mean squared error of simple linear regression model is : " + str(RMSE_Lin_Reg_Tran_Pred))
The mean absolute error of simple linear regression model is : 0.40218792597116115
The mean squared error of simple linear regression model is : 0.3312621055120514
The root mean squared error of simple linear regression model is : 0.5755537381618256

Model 1b: Linear regression using stochastic gradient descent without regularization

In [88]:
sgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1)
sgd_reg.fit(X_train, y_train.ravel())
/usr/local/anaconda/python2/lib/python2.7/site-packages/sklearn/linear_model/stochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.
  DeprecationWarning)
Out[88]:
SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.1,
       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
       loss='squared_loss', max_iter=None, n_iter=50, penalty=None,
       power_t=0.25, random_state=None, shuffle=True, tol=None, verbose=0,
       warm_start=False)
In [89]:
print(sgd_reg.intercept_)
print(sgd_reg.coef_)
[-0.08731821]
[-0.10752723  1.58110804  0.02789466 -0.60399679  0.15531906  0.0563204
 -1.1002516  -0.20371198 -0.08731821 -0.35567218  0.26249513  0.0563204
 -0.05046155  0.06072164  0.21741115  0.06668207  0.19474665  0.26249513
  0.18487407  0.2228247  -0.43103156  0.23530325  0.10253378  0.05669403
 -0.29278876 -0.20621574  0.0563204   0.07197847  0.32051734 -1.36285083
  0.26960964 -0.03412233 -0.08302129  0.10099546 -0.24463407  0.0563204
 -0.23607443 -0.28626179 -0.09884285 -0.08598411 -0.06500686 -0.08311052
 -0.19800274 -0.02133331  0.02721005 -0.181897   -0.05557463  1.19755999
 -0.09065862 -0.42776724  1.05299553  0.8362252  -0.69419267 -0.68241543
 -0.08150497]
In [90]:
# Checking the coefficients for each of the independent attributes

for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, sgd_reg.coef_[idx]))
The coefficient for dcoilwtico is -0.107527229905
The coefficient for type_Additional is 1.58110804062
The coefficient for type_Bridge is 0.0278946628035
The coefficient for type_Event is -0.603996785363
The coefficient for type_Holiday is 0.155319064305
The coefficient for type_No_Holiday is 0.0563203950128
The coefficient for type_Transfer is -1.10025160154
The coefficient for type_Work Day is -0.203711983549
The coefficient for store_nbr_44 is -0.0873182077152
The coefficient for locale_Local is -0.355672184674
The coefficient for locale_National is 0.262495134947
The coefficient for locale_None is 0.0563203950128
The coefficient for locale_Regional is -0.0504615530009
The coefficient for locale_name_Ambato is 0.0607216373621
The coefficient for locale_name_Cayambe is 0.217411153326
The coefficient for locale_name_Cotopaxi is 0.0666820696582
The coefficient for locale_name_Cuenca is 0.194746649717
The coefficient for locale_name_Ecuador is 0.262495134947
The coefficient for locale_name_Esmeraldas is 0.18487406833
The coefficient for locale_name_Guaranda is 0.222824696627
The coefficient for locale_name_Guayaquil is -0.431031561902
The coefficient for locale_name_Ibarra is 0.235303247717
The coefficient for locale_name_Latacunga is 0.102533777191
The coefficient for locale_name_Libertad is 0.0566940337269
The coefficient for locale_name_Loja is -0.292788763201
The coefficient for locale_name_Manta is -0.206215743692
The coefficient for locale_name_None is 0.0563203950128
The coefficient for locale_name_Puyo is 0.0719784654896
The coefficient for locale_name_Quevedo is 0.320517336189
The coefficient for locale_name_Quito is -1.36285082614
The coefficient for locale_name_Riobamba is 0.269609644592
The coefficient for locale_name_Santa Elena is -0.0341223295429
The coefficient for locale_name_Santo Domingo de los Tsachilas is -0.0830212931161
The coefficient for transferred_False is 0.100995464151
The coefficient for transferred_True is -0.244634066879
The coefficient for transferred_None is 0.0563203950128
The coefficient for month_01 is -0.236074433583
The coefficient for month_02 is -0.286261794733
The coefficient for month_03 is -0.0988428482997
The coefficient for month_04 is -0.0859841114542
The coefficient for month_05 is -0.0650068595127
The coefficient for month_06 is -0.0831105233052
The coefficient for month_07 is -0.198002736744
The coefficient for month_08 is -0.0213333113379
The coefficient for month_09 is 0.0272100524486
The coefficient for month_10 is -0.181896999279
The coefficient for month_11 is -0.0555746330339
The coefficient for month_12 is 1.19755999112
The coefficient for day_Friday is -0.090658624402
The coefficient for day_Monday is -0.427767239733
The coefficient for day_Saturday is 1.05299553286
The coefficient for day_Sunday is 0.836225196875
The coefficient for day_Thursday is -0.694192673304
The coefficient for day_Tuesday is -0.682415429688
The coefficient for day_Wednesday is -0.0815049703241
In [91]:
sgd_reg.score(X_test, Y_test)
Out[91]:
0.68279259232856
In [92]:
# checking the sum of squared errors by predicting value of y for test cases and 
# subtracting from the actual y for the test cases
mse = np.mean((sgd_reg.predict(X_test)-Y_test)**2)
In [93]:
# underroot of mean_sq_error is standard deviation i.e. avg variance between predicted and actual
import math
math.sqrt(mse)
Out[93]:
0.5525769808686058
In [94]:
# predict transaction volume for a set of attributes 
y_pred = sgd_reg.predict(X_test)
In [95]:
# Since this is regression, plot the predicted y value vs actual y values for the test data
plt.scatter(Y_test, y_pred)
Out[95]:
<matplotlib.collections.PathCollection at 0x7f74d967f390>
In [96]:
from sklearn.metrics import r2_score
R2_Sgd_Tran_Pred = r2_score(Y_test, y_pred)
print(R2_Sgd_Tran_Pred)
0.68279259232856
In [97]:
y_train_pred = sgd_reg.predict(X_train)
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Train data
print(mean_absolute_error(y_train, y_train_pred))
print(mean_squared_error(y_train, y_train_pred))
print(np.sqrt(mean_squared_error(y_train, y_train_pred)))
0.360029398818339
0.25731219174288633
0.507259491525675
In [98]:
RMSE_Sgd_Tran_Pred = np.sqrt(mean_squared_error(Y_test, y_pred))
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of simple linear regression model is : " + str(mean_absolute_error(Y_test, y_pred)))
print("The mean squared error of simple linear regression model is : " + str(mean_squared_error(Y_test, y_pred)))
print("The root mean squared error of simple linear regression model is : " + str(RMSE_Sgd_Tran_Pred))
The mean absolute error of simple linear regression model is : 0.40013647072467945
The mean squared error of simple linear regression model is : 0.30534131978586365
The root mean squared error of simple linear regression model is : 0.5525769808686058

Model 2 : Decision Tree with and without gridsearch CV

In [99]:
# Criterion : Mean absolute error
In [100]:
dtree = DecisionTreeRegressor(random_state=0, criterion="mae")
dtree_fit = dtree.fit(X_train, y_train)
In [101]:
dtree_scores = cross_val_score(dtree_fit, X_train, y_train, cv = 5)
print("mean cross validation score: {}".format(np.mean(dtree_scores)))
print("score without cv: {}".format(dtree_fit.score(X_train, y_train)))
mean cross validation score: 0.406187440504
score without cv: 1.0
In [102]:
# on the test or hold-out set
from sklearn.metrics import r2_score
y_pred = dtree_fit.predict(X_test)
R2_DT_WoutGCV_Tran_Pred = r2_score(Y_test, y_pred)
print(R2_DT_WoutGCV_Tran_Pred)
print(dtree_fit.score(X_test, Y_test))
0.5605361928119182
0.5605361928119182
In [103]:
final_mae = mean_absolute_error(Y_test, y_pred)
final_mse = mean_squared_error(Y_test, y_pred)
final_rmse = np.sqrt(final_mse)
In [104]:
RMSE_DT_WoutGCV_Tran_Pred = final_rmse
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of decision tree without gridsearchcv is : " + str(final_mae))
print("The mean squared error of decision tree without gridsearchcv is : " + str(final_mse))
print("The root mean squared error of decision tree without gridsearchcv is : " + str(final_rmse))
The mean absolute error of decision tree without gridsearchcv is : 0.45099078356098804
The mean squared error of decision tree without gridsearchcv is : 0.42302435453814535
The root mean squared error of decision tree without gridsearchcv is : 0.6504032245754516
In [105]:
scoring = make_scorer(r2_score)
g_cv = GridSearchCV(DecisionTreeRegressor(random_state=0),
              param_grid={'min_samples_split': range(2, 10)},
              scoring=scoring, cv=5, refit=True)

g_cv.fit(X_train, y_train)

result = g_cv.cv_results_
# print(result)
R2_DT_WithGCV_Tran_Pred = r2_score(Y_test, g_cv.best_estimator_.predict(X_test))
print(R2_DT_WithGCV_Tran_Pred)
0.6682680307699073
In [106]:
y_pred = g_cv.best_estimator_.predict(X_test)
In [107]:
final_mae = mean_absolute_error(Y_test, y_pred)
final_mse = mean_squared_error(Y_test, y_pred)
final_rmse = np.sqrt(final_mse)
In [108]:
RMSE_DT_WithGCV_Tran_Pred = final_rmse
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of decision tree with gridsearchcv is : " + str(final_mae))
print("The mean squared error of decision tree with gridsearchcv is : " + str(final_mse))
print("The root mean squared error of decision tree with gridsearchcv is : " + str(final_rmse))
The mean absolute error of decision tree with gridsearchcv is : 0.3987709312490536
The mean squared error of decision tree with gridsearchcv is : 0.3193225468580377
The root mean squared error of decision tree with gridsearchcv is : 0.5650863180594958
In [109]:
print("The best params from gridsearchcv are :" + str(g_cv.best_params_))
The best params from gridsearchcv are :{'min_samples_split': 8}
In [110]:
print("The best estimators from gridsearchcv are :" + str(g_cv.best_estimator_))
The best estimators from gridsearchcv are :DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
           max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=1,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           presort=False, random_state=0, splitter='best')
In [111]:
# Printing the decision tree
dot_data = export_graphviz(dtree_fit, out_file=None)
In [112]:
graph = graphviz.Source(dot_data) 
In [113]:
dot_data = export_graphviz(dtree_fit, out_file=None, 
                         feature_names=X_train.columns,   
                         filled=True, rounded=True,  
                         special_characters=True)
In [114]:
graph = graphviz.Source(dot_data)
graph 

# Inference:
# Oil price is the main predictor value using which decision tree is built and is an influencing factor in the total transaction volume.
Out[114]:
Tree 0 day_Saturday ≤ 0.5 mae = 0.752 samples = 707 value = -0.236 1 day_Sunday ≤ 0.5 mae = 0.642 samples = 598 value = -0.401 0->1 True 1194 month_12 ≤ 0.5 mae = 0.48 samples = 109 value = 0.971 0->1194 False 2 month_12 ≤ 0.5 mae = 0.514 samples = 497 value = -0.514 1->2 993 dcoilwtico ≤ 0.625 mae = 0.517 samples = 101 value = 0.84 1->993 3 day_Friday ≤ 0.5 mae = 0.367 samples = 451 value = -0.592 2->3 902 locale_National ≤ 0.5 mae = 1.078 samples = 46 value = 0.839 2->902 4 day_Wednesday ≤ 0.5 mae = 0.334 samples = 355 value = -0.677 3->4 713 dcoilwtico ≤ 0.426 mae = 0.309 samples = 96 value = -0.21 3->713 5 day_Monday ≤ 0.5 mae = 0.288 samples = 274 value = -0.763 4->5 552 dcoilwtico ≤ 0.52 mae = 0.224 samples = 81 value = -0.263 4->552 6 dcoilwtico ≤ 0.231 mae = 0.276 samples = 182 value = -0.861 5->6 369 dcoilwtico ≤ -0.143 mae = 0.265 samples = 92 value = -0.649 5->369 7 locale_National ≤ 0.5 mae = 0.235 samples = 71 value = -0.753 6->7 148 locale_None ≤ 0.5 mae = 0.288 samples = 111 value = -0.944 6->148 8 locale_name_Ambato ≤ 0.5 mae = 0.221 samples = 70 value = -0.767 7->8 147 mae = 0.0 samples = 1 value = 0.471 7->147 9 dcoilwtico ≤ -1.529 mae = 0.21 samples = 69 value = -0.753 8->9 146 mae = 0.0 samples = 1 value = -1.732 8->146 10 dcoilwtico ≤ -1.542 mae = 0.099 samples = 7 value = -0.608 9->10 23 dcoilwtico ≤ -1.336 mae = 0.211 samples = 62 value = -0.799 9->23 11 month_08 ≤ 0.5 mae = 0.09 samples = 6 value = -0.608 10->11 22 mae = 0.0 samples = 1 value = -0.455 10->22 12 dcoilwtico ≤ -1.631 mae = 0.07 samples = 3 value = -0.514 11->12 17 day_Thursday ≤ 0.5 mae = 0.079 samples = 3 value = -0.609 11->17 13 mae = 0.0 samples = 1 value = -0.689 12->13 14 day_Tuesday ≤ 0.5 mae = 0.017 samples = 2 value = -0.497 12->14 15 mae = 0.0 samples = 1 value = -0.514 14->15 16 mae = 0.0 samples = 1 value = -0.48 14->16 18 dcoilwtico ≤ -1.625 mae = 0.001 samples = 2 value = -0.608 17->18 21 mae = 0.0 samples = 1 value = -0.844 17->21 19 mae = 0.0 samples = 1 value = -0.608 18->19 20 mae = 0.0 samples = 1 value = -0.609 18->20 24 month_08 ≤ 0.5 mae = 0.238 samples = 22 value = -0.93 23->24 67 locale_name_Libertad ≤ 0.5 mae = 0.176 samples = 40 value = -0.751 23->67 25 locale_name_Guaranda ≤ 0.5 mae = 0.231 samples = 20 value = -0.935 24->25 64 dcoilwtico ≤ -1.444 mae = 0.113 samples = 2 value = -0.621 24->64 26 month_09 ≤ 0.5 mae = 0.222 samples = 19 value = -0.937 25->26 63 mae = 0.0 samples = 1 value = -0.541 25->63 27 dcoilwtico ≤ -1.438 mae = 0.2 samples = 13 value = -1.02 26->27 52 dcoilwtico ≤ -1.41 mae = 0.247 samples = 6 value = -0.831 26->52 28 month_01 ≤ 0.5 mae = 0.107 samples = 5 value = -0.928 27->28 37 month_01 ≤ 0.5 mae = 0.227 samples = 8 value = -1.055 27->37 29 dcoilwtico ≤ -1.46 mae = 0.084 samples = 4 value = -0.909 28->29 36 mae = 0.0 samples = 1 value = -1.124 28->36 30 dcoilwtico ≤ -1.492 mae = 0.089 samples = 3 value = -0.889 29->30 35 mae = 0.0 samples = 1 value = -0.96 29->35 31 dcoilwtico ≤ -1.524 mae = 0.019 samples = 2 value = -0.909 30->31 34 mae = 0.0 samples = 1 value = -0.661 30->34 32 mae = 0.0 samples = 1 value = -0.889 31->32 33 mae = 0.0 samples = 1 value = -0.928 31->33 38 day_Tuesday ≤ 0.5 mae = 0.256 samples = 5 value = -1.124 37->38 47 day_Tuesday ≤ 0.5 mae = 0.109 samples = 3 value = -0.916 37->47 39 dcoilwtico ≤ -1.403 mae = 0.048 samples = 3 value = -1.038 38->39 44 dcoilwtico ≤ -1.4 mae = 0.523 samples = 2 value = -1.648 38->44 40 mae = 0.0 samples = 1 value = -1.164 39->40 41 dcoilwtico ≤ -1.369 mae = 0.009 samples = 2 value = -1.029 39->41 42 mae = 0.0 samples = 1 value = -1.02 41->42 43 mae = 0.0 samples = 1 value = -1.038 41->43 45 mae = 0.0 samples = 1 value = -1.124 44->45 46 mae = 0.0 samples = 1 value = -2.171 44->46 48 mae = 0.0 samples = 1 value = -0.745 47->48 49 dcoilwtico ≤ -1.398 mae = 0.078 samples = 2 value = -0.994 47->49 50 mae = 0.0 samples = 1 value = -1.072 49->50 51 mae = 0.0 samples = 1 value = -0.916 49->51 53 dcoilwtico ≤ -1.446 mae = 0.27 samples = 3 value = -0.729 52->53 58 day_Tuesday ≤ 0.5 mae = 0.155 samples = 3 value = -0.937 52->58 54 mae = 0.0 samples = 1 value = -0.932 53->54 55 dcoilwtico ≤ -1.426 mae = 0.303 samples = 2 value = -0.426 53->55 56 mae = 0.0 samples = 1 value = -0.123 55->56 57 mae = 0.0 samples = 1 value = -0.729 55->57 59 dcoilwtico ≤ -1.377 mae = 0.214 samples = 2 value = -0.724 58->59 62 mae = 0.0 samples = 1 value = -0.976 58->62 60 mae = 0.0 samples = 1 value = -0.51 59->60 61 mae = 0.0 samples = 1 value = -0.937 59->61 65 mae = 0.0 samples = 1 value = -0.735 64->65 66 mae = 0.0 samples = 1 value = -0.508 64->66 68 month_01 ≤ 0.5 mae = 0.169 samples = 39 value = -0.748 67->68 145 mae = 0.0 samples = 1 value = -1.192 67->145 69 dcoilwtico ≤ -0.793 mae = 0.161 samples = 37 value = -0.753 68->69 142 dcoilwtico ≤ -1.312 mae = 0.116 samples = 2 value = -0.435 68->142 70 dcoilwtico ≤ -0.8 mae = 0.169 samples = 27 value = -0.807 69->70 123 locale_name_None ≤ 0.5 mae = 0.111 samples = 10 value = -0.678 69->123 71 month_02 ≤ 0.5 mae = 0.159 samples = 26 value = -0.801 70->71 122 mae = 0.0 samples = 1 value = -1.245 70->122 72 month_03 ≤ 0.5 mae = 0.168 samples = 21 value = -0.831 71->72 113 dcoilwtico ≤ -1.211 mae = 0.077 samples = 5 value = -0.729 71->113 73 dcoilwtico ≤ -0.838 mae = 0.166 samples = 20 value = -0.831 72->73 112 mae = 0.0 samples = 1 value = -0.614 72->112 74 month_06 ≤ 0.5 mae = 0.179 samples = 16 value = -0.858 73->74 105 dcoilwtico ≤ -0.818 mae = 0.072 samples = 4 value = -0.731 73->105 75 dcoilwtico ≤ -0.97 mae = 0.164 samples = 15 value = -0.855 74->75 104 mae = 0.0 samples = 1 value = -1.253 74->104 76 dcoilwtico ≤ -0.993 mae = 0.161 samples = 11 value = -0.832 75->76 97 month_07 ≤ 0.5 mae = 0.121 samples = 4 value = -0.951 75->97 77 dcoilwtico ≤ -1.315 mae = 0.158 samples = 10 value = -0.843 76->77 96 mae = 0.0 samples = 1 value = -0.65 76->96 78 mae = 0.0 samples = 1 value = -0.972 77->78 79 dcoilwtico ≤ -1.275 mae = 0.161 samples = 9 value = -0.832 77->79 80 month_04 ≤ 0.5 mae = 0.313 samples = 3 value = -0.65 79->80 85 month_10 ≤ 0.5 mae = 0.054 samples = 6 value = -0.843 79->85 81 month_10 ≤ 0.5 mae = 0.128 samples = 2 value = -0.779 80->81 84 mae = 0.0 samples = 1 value = 0.032 80->84 82 mae = 0.0 samples = 1 value = -0.907 81->82 83 mae = 0.0 samples = 1 value = -0.65 81->83 86 dcoilwtico ≤ -1.163 mae = 0.048 samples = 5 value = -0.832 85->86 95 mae = 0.0 samples = 1 value = -0.916 85->95 87 dcoilwtico ≤ -1.21 mae = 0.062 samples = 3 value = -0.831 86->87 92 locale_None ≤ 0.5 mae = 0.003 samples = 2 value = -0.858 86->92 88 day_Thursday ≤ 0.5 mae = 0.001 samples = 2 value = -0.831 87->88 91 mae = 0.0 samples = 1 value = -0.645 87->91 89 mae = 0.0 samples = 1 value = -0.831 88->89 90 mae = 0.0 samples = 1 value = -0.832 88->90 93 mae = 0.0 samples = 1 value = -0.855 92->93 94 mae = 0.0 samples = 1 value = -0.861 92->94 98 day_Thursday ≤ 0.5 mae = 0.012 samples = 3 value = -0.953 97->98 103 mae = 0.0 samples = 1 value = -0.508 97->103 99 mae = 0.0 samples = 1 value = -0.986 98->99 100 dcoilwtico ≤ -0.947 mae = 0.002 samples = 2 value = -0.951 98->100 101 mae = 0.0 samples = 1 value = -0.953 100->101 102 mae = 0.0 samples = 1 value = -0.949 100->102 106 dcoilwtico ≤ -0.827 mae = 0.01 samples = 2 value = -0.656 105->106 109 locale_name_None ≤ 0.5 mae = 0.006 samples = 2 value = -0.801 105->109 107 mae = 0.0 samples = 1 value = -0.666 106->107 108 mae = 0.0 samples = 1 value = -0.646 106->108 110 mae = 0.0 samples = 1 value = -0.807 109->110 111 mae = 0.0 samples = 1 value = -0.795 109->111 114 dcoilwtico ≤ -1.276 mae = 0.044 samples = 3 value = -0.753 113->114 119 day_Thursday ≤ 0.5 mae = 0.081 samples = 2 value = -0.613 113->119 115 mae = 0.0 samples = 1 value = -0.729 114->115 116 day_Tuesday ≤ 0.5 mae = 0.053 samples = 2 value = -0.807 114->116 117 mae = 0.0 samples = 1 value = -0.753 116->117 118 mae = 0.0 samples = 1 value = -0.86 116->118 120 mae = 0.0 samples = 1 value = -0.694 119->120 121 mae = 0.0 samples = 1 value = -0.532 119->121 124 dcoilwtico ≤ -0.437 mae = 0.042 samples = 3 value = -0.781 123->124 129 month_06 ≤ 0.5 mae = 0.122 samples = 7 value = -0.65 123->129 125 mae = 0.0 samples = 1 value = -0.677 124->125 126 locale_Local ≤ 0.5 mae = 0.011 samples = 2 value = -0.792 124->126 127 mae = 0.0 samples = 1 value = -0.803 126->127 128 mae = 0.0 samples = 1 value = -0.781 126->128 130 dcoilwtico ≤ 0.107 mae = 0.099 samples = 6 value = -0.664 129->130 141 mae = 0.0 samples = 1 value = -0.393 129->141 131 day_Tuesday ≤ 0.5 mae = 0.088 samples = 4 value = -0.642 130->131 138 day_Thursday ≤ 0.5 mae = 0.092 samples = 2 value = -0.771 130->138 132 mae = 0.0 samples = 1 value = -0.411 131->132 133 month_11 ≤ 0.5 mae = 0.038 samples = 3 value = -0.65 131->133 134 dcoilwtico ≤ 0.065 mae = 0.049 samples = 2 value = -0.699 133->134 137 mae = 0.0 samples = 1 value = -0.633 133->137 135 mae = 0.0 samples = 1 value = -0.748 134->135 136 mae = 0.0 samples = 1 value = -0.65 134->136 139 mae = 0.0 samples = 1 value = -0.678 138->139 140 mae = 0.0 samples = 1 value = -0.863 138->140 143 mae = 0.0 samples = 1 value = -0.552 142->143 144 mae = 0.0 samples = 1 value = -0.319 142->144 149 month_05 ≤ 0.5 mae = 0.377 samples = 9 value = -0.676 148->149 166 dcoilwtico ≤ 0.527 mae = 0.273 samples = 102 value = -0.968 148->166 150 month_07 ≤ 0.5 mae = 0.203 samples = 8 value = -0.718 149->150 165 mae = 0.0 samples = 1 value = 1.092 149->165 151 locale_name_Quevedo ≤ 0.5 mae = 0.111 samples = 4 value = -0.584 150->151 158 type_Additional ≤ 0.5 mae = 0.1 samples = 4 value = -0.955 150->158 152 day_Tuesday ≤ 0.5 mae = 0.038 samples = 3 value = -0.606 151->152 157 mae = 0.0 samples = 1 value = -0.274 151->157 153 mae = 0.0 samples = 1 value = -0.676 152->153 154 locale_name_Ecuador ≤ 0.5 mae = 0.022 samples = 2 value = -0.584 152->154 155 mae = 0.0 samples = 1 value = -0.606 154->155 156 mae = 0.0 samples = 1 value = -0.562 154->156 159 locale_National ≤ 0.5 mae = 0.053 samples = 3 value = -1.0 158->159 164 mae = 0.0 samples = 1 value = -0.76 158->164 160 locale_name_Guayaquil ≤ 0.5 mae = 0.035 samples = 2 value = -1.035 159->160 163 mae = 0.0 samples = 1 value = -0.911 159->163 161 mae = 0.0 samples = 1 value = -1.0 160->161 162 mae = 0.0 samples = 1 value = -1.07 160->162 167 dcoilwtico ≤ 0.514 mae = 0.148 samples = 12 value = -1.15 166->167 190 dcoilwtico ≤ 0.538 mae = 0.282 samples = 90 value = -0.926 166->190 168 month_04 ≤ 0.5 mae = 0.134 samples = 9 value = -1.034 167->168 185 month_02 ≤ 0.5 mae = 0.054 samples = 3 value = -1.269 167->185 169 month_02 ≤ 0.5 mae = 0.105 samples = 6 value = -1.007 168->169 180 day_Tuesday ≤ 0.5 mae = 0.08 samples = 3 value = -1.192 168->180 170 day_Tuesday ≤ 0.5 mae = 0.085 samples = 5 value = -0.992 169->170 179 mae = 0.0 samples = 1 value = -1.201 169->179 171 month_03 ≤ 0.5 mae = 0.077 samples = 3 value = -0.828 170->171 176 month_03 ≤ 0.5 mae = 0.015 samples = 2 value = -1.007 170->176 172 month_01 ≤ 0.5 mae = 0.012 samples = 2 value = -0.816 171->172 175 mae = 0.0 samples = 1 value = -1.034 171->175 173 mae = 0.0 samples = 1 value = -0.828 172->173 174 mae = 0.0 samples = 1 value = -0.804 172->174 177 mae = 0.0 samples = 1 value = -1.022 176->177 178 mae = 0.0 samples = 1 value = -0.992 176->178 181 mae = 0.0 samples = 1 value = -1.347 180->181 182 dcoilwtico ≤ 0.373 mae = 0.043 samples = 2 value = -1.15 180->182 183 mae = 0.0 samples = 1 value = -1.107 182->183 184 mae = 0.0 samples = 1 value = -1.192 182->184 186 day_Tuesday ≤ 0.5 mae = 0.037 samples = 2 value = -1.231 185->186 189 mae = 0.0 samples = 1 value = -1.355 185->189 187 mae = 0.0 samples = 1 value = -1.269 186->187 188 mae = 0.0 samples = 1 value = -1.194 186->188 191 dcoilwtico ≤ 0.537 mae = 0.256 samples = 3 value = -0.318 190->191 196 month_10 ≤ 0.5 mae = 0.266 samples = 87 value = -0.944 190->196 192 day_Thursday ≤ 0.5 mae = 0.094 samples = 2 value = -0.412 191->192 195 mae = 0.0 samples = 1 value = 0.262 191->195 193 mae = 0.0 samples = 1 value = -0.318 192->193 194 mae = 0.0 samples = 1 value = -0.506 192->194 197 dcoilwtico ≤ 1.116 mae = 0.271 samples = 78 value = -0.913 196->197 352 dcoilwtico ≤ 0.747 mae = 0.118 samples = 9 value = -1.142 196->352 198 dcoilwtico ≤ 1.036 mae = 0.271 samples = 73 value = -0.925 197->198 343 dcoilwtico ≤ 1.176 mae = 0.18 samples = 5 value = -0.705 197->343 199 month_08 ≤ 0.5 mae = 0.281 samples = 65 value = -0.888 198->199 328 month_09 ≤ 0.5 mae = 0.139 samples = 8 value = -1.026 198->328 200 dcoilwtico ≤ 0.899 mae = 0.255 samples = 60 value = -0.902 199->200 319 dcoilwtico ≤ 0.848 mae = 0.354 samples = 5 value = -0.182 199->319 201 month_09 ≤ 0.5 mae = 0.252 samples = 44 value = -0.935 200->201 288 month_09 ≤ 0.5 mae = 0.203 samples = 16 value = -0.743 200->288 202 dcoilwtico ≤ 0.738 mae = 0.233 samples = 41 value = -0.959 201->202 283 dcoilwtico ≤ 0.554 mae = 0.136 samples = 3 value = -0.349 201->283 203 dcoilwtico ≤ 0.568 mae = 0.259 samples = 31 value = -1.044 202->203 264 dcoilwtico ≤ 0.863 mae = 0.085 samples = 10 value = -0.873 202->264 204 month_11 ≤ 0.5 mae = 0.087 samples = 12 value = -0.935 203->204 227 month_02 ≤ 0.5 mae = 0.313 samples = 19 value = -1.207 203->227 205 month_01 ≤ 0.5 mae = 0.081 samples = 9 value = -0.978 204->205 222 dcoilwtico ≤ 0.556 mae = 0.025 samples = 3 value = -0.839 204->222 206 dcoilwtico ≤ 0.553 mae = 0.051 samples = 4 value = -1.012 205->206 213 dcoilwtico ≤ 0.558 mae = 0.087 samples = 5 value = -0.944 205->213 207 mae = 0.0 samples = 1 value = -0.925 206->207 208 month_04 ≤ 0.5 mae = 0.037 samples = 3 value = -1.018 206->208 209 month_05 ≤ 0.5 mae = 0.006 samples = 2 value = -1.012 208->209 212 mae = 0.0 samples = 1 value = -1.116 208->212 210 mae = 0.0 samples = 1 value = -1.018 209->210 211 mae = 0.0 samples = 1 value = -1.006 209->211 214 dcoilwtico ≤ 0.552 mae = 0.054 samples = 4 value = -0.961 213->214 221 mae = 0.0 samples = 1 value = -0.727 213->221 215 dcoilwtico ≤ 0.55 mae = 0.041 samples = 3 value = -0.978 214->215 220 mae = 0.0 samples = 1 value = -0.884 214->220 216 day_Tuesday ≤ 0.5 mae = 0.017 samples = 2 value = -0.961 215->216 219 mae = 0.0 samples = 1 value = -1.066 215->219 217 mae = 0.0 samples = 1 value = -0.944 216->217 218 mae = 0.0 samples = 1 value = -0.978 216->218 223 mae = 0.0 samples = 1 value = -0.819 222->223 224 dcoilwtico ≤ 0.557 mae = 0.028 samples = 2 value = -0.867 222->224 225 mae = 0.0 samples = 1 value = -0.895 224->225 226 mae = 0.0 samples = 1 value = -0.839 224->226 228 day_Thursday ≤ 0.5 mae = 0.308 samples = 16 value = -1.213 227->228 259 dcoilwtico ≤ 0.705 mae = 0.229 samples = 3 value = -0.864 227->259 229 month_06 ≤ 0.5 mae = 0.377 samples = 8 value = -1.265 228->229 244 month_03 ≤ 0.5 mae = 0.222 samples = 8 value = -1.122 228->244 230 month_03 ≤ 0.5 mae = 0.245 samples = 7 value = -1.226 229->230 243 mae = 0.0 samples = 1 value = -2.52 229->243 231 dcoilwtico ≤ 0.658 mae = 0.142 samples = 6 value = -1.217 230->231 242 mae = 0.0 samples = 1 value = -2.095 230->242 232 month_05 ≤ 0.5 mae = 0.154 samples = 4 value = -1.15 231->232 239 dcoilwtico ≤ 0.701 mae = 0.021 samples = 2 value = -1.325 231->239 233 mae = 0.0 samples = 1 value = -0.727 232->233 234 dcoilwtico ≤ 0.628 mae = 0.045 samples = 3 value = -1.207 232->234 235 dcoilwtico ≤ 0.595 mae = 0.009 samples = 2 value = -1.217 234->235 238 mae = 0.0 samples = 1 value = -1.092 234->238 236 mae = 0.0 samples = 1 value = -1.207 235->236 237 mae = 0.0 samples = 1 value = -1.226 235->237 240 mae = 0.0 samples = 1 value = -1.303 239->240 241 mae = 0.0 samples = 1 value = -1.346 239->241 245 month_05 ≤ 0.5 mae = 0.133 samples = 7 value = -1.155 244->245 258 mae = 0.0 samples = 1 value = -0.314 244->258 246 dcoilwtico ≤ 0.668 mae = 0.082 samples = 6 value = -1.187 245->246 257 mae = 0.0 samples = 1 value = -0.713 245->257 247 dcoilwtico ≤ 0.573 mae = 0.061 samples = 3 value = -1.219 246->247 252 dcoilwtico ≤ 0.7 mae = 0.059 samples = 3 value = -1.09 246->252 248 mae = 0.0 samples = 1 value = -1.155 247->248 249 dcoilwtico ≤ 0.609 mae = 0.059 samples = 2 value = -1.279 247->249 250 mae = 0.0 samples = 1 value = -1.338 249->250 251 mae = 0.0 samples = 1 value = -1.219 249->251 253 mae = 0.0 samples = 1 value = -1.044 252->253 254 dcoilwtico ≤ 0.72 mae = 0.066 samples = 2 value = -1.156 252->254 255 mae = 0.0 samples = 1 value = -1.222 254->255 256 mae = 0.0 samples = 1 value = -1.09 254->256 260 day_Tuesday ≤ 0.5 mae = 0.3 samples = 2 value = -1.164 259->260 263 mae = 0.0 samples = 1 value = -0.779 259->263 261 mae = 0.0 samples = 1 value = -0.864 260->261 262 mae = 0.0 samples = 1 value = -1.465 260->262 265 month_03 ≤ 0.5 mae = 0.067 samples = 8 value = -0.838 264->265 280 day_Tuesday ≤ 0.5 mae = 0.053 samples = 2 value = -1.012 264->280 266 month_01 ≤ 0.5 mae = 0.063 samples = 6 value = -0.815 265->266 277 day_Tuesday ≤ 0.5 mae = 0.011 samples = 2 value = -0.899 265->277 267 day_Thursday ≤ 0.5 mae = 0.054 samples = 5 value = -0.812 266->267 276 mae = 0.0 samples = 1 value = -0.917 266->276 268 dcoilwtico ≤ 0.82 mae = 0.023 samples = 2 value = -0.835 267->268 271 month_07 ≤ 0.5 mae = 0.055 samples = 3 value = -0.751 267->271 269 mae = 0.0 samples = 1 value = -0.812 268->269 270 mae = 0.0 samples = 1 value = -0.857 268->270 272 month_02 ≤ 0.5 mae = 0.034 samples = 2 value = -0.785 271->272 275 mae = 0.0 samples = 1 value = -0.653 271->275 273 mae = 0.0 samples = 1 value = -0.751 272->273 274 mae = 0.0 samples = 1 value = -0.819 272->274 278 mae = 0.0 samples = 1 value = -0.888 277->278 279 mae = 0.0 samples = 1 value = -0.909 277->279 281 mae = 0.0 samples = 1 value = -1.066 280->281 282 mae = 0.0 samples = 1 value = -0.959 280->282 284 mae = 0.0 samples = 1 value = -0.674 283->284 285 day_Tuesday ≤ 0.5 mae = 0.041 samples = 2 value = -0.308 283->285 286 mae = 0.0 samples = 1 value = -0.349 285->286 287 mae = 0.0 samples = 1 value = -0.267 285->287 289 month_06 ≤ 0.5 mae = 0.166 samples = 14 value = -0.727 288->289 316 day_Tuesday ≤ 0.5 mae = 0.039 samples = 2 value = -1.205 288->316 290 day_Thursday ≤ 0.5 mae = 0.163 samples = 13 value = -0.743 289->290 315 mae = 0.0 samples = 1 value = -0.53 289->315 291 dcoilwtico ≤ 0.931 mae = 0.168 samples = 6 value = -0.887 290->291 302 dcoilwtico ≤ 1.01 mae = 0.138 samples = 7 value = -0.696 290->302 292 month_05 ≤ 0.5 mae = 0.036 samples = 2 value = -0.708 291->292 295 dcoilwtico ≤ 1.005 mae = 0.097 samples = 4 value = -1.039 291->295 293 mae = 0.0 samples = 1 value = -0.672 292->293 294 mae = 0.0 samples = 1 value = -0.744 292->294 296 month_05 ≤ 0.5 mae = 0.028 samples = 3 value = -1.06 295->296 301 mae = 0.0 samples = 1 value = -0.757 295->301 297 dcoilwtico ≤ 0.968 mae = 0.021 samples = 2 value = -1.082 296->297 300 mae = 0.0 samples = 1 value = -1.018 296->300 298 mae = 0.0 samples = 1 value = -1.103 297->298 299 mae = 0.0 samples = 1 value = -1.06 297->299 303 dcoilwtico ≤ 0.953 mae = 0.05 samples = 5 value = -0.71 302->303 312 month_04 ≤ 0.5 mae = 0.289 samples = 2 value = -0.347 302->312 304 month_02 ≤ 0.5 mae = 0.071 samples = 2 value = -0.64 303->304 307 dcoilwtico ≤ 1.004 mae = 0.025 samples = 3 value = -0.743 303->307 305 mae = 0.0 samples = 1 value = -0.71 304->305 306 mae = 0.0 samples = 1 value = -0.569 304->306 308 month_05 ≤ 0.5 mae = 0.015 samples = 2 value = -0.757 307->308 311 mae = 0.0 samples = 1 value = -0.696 307->311 309 mae = 0.0 samples = 1 value = -0.772 308->309 310 mae = 0.0 samples = 1 value = -0.743 308->310 313 mae = 0.0 samples = 1 value = -0.636 312->313 314 mae = 0.0 samples = 1 value = -0.058 312->314 317 mae = 0.0 samples = 1 value = -1.243 316->317 318 mae = 0.0 samples = 1 value = -1.166 316->318 320 dcoilwtico ≤ 0.688 mae = 0.152 samples = 4 value = -0.176 319->320 327 mae = 0.0 samples = 1 value = -1.345 319->327 321 day_Thursday ≤ 0.5 mae = 0.045 samples = 3 value = -0.171 320->321 326 mae = 0.0 samples = 1 value = -0.642 320->326 322 dcoilwtico ≤ 0.626 mae = 0.005 samples = 2 value = -0.176 321->322 325 mae = 0.0 samples = 1 value = -0.046 321->325 323 mae = 0.0 samples = 1 value = -0.182 322->323 324 mae = 0.0 samples = 1 value = -0.171 322->324 329 dcoilwtico ≤ 1.038 mae = 0.067 samples = 6 value = -0.981 328->329 340 dcoilwtico ≤ 1.073 mae = 0.015 samples = 2 value = -1.339 328->340 330 mae = 0.0 samples = 1 value = -1.201 329->330 331 month_08 ≤ 0.5 mae = 0.036 samples = 5 value = -0.976 329->331 332 day_Tuesday ≤ 0.5 mae = 0.016 samples = 3 value = -0.945 331->332 337 dcoilwtico ≤ 1.083 mae = 0.041 samples = 2 value = -1.026 331->337 333 mae = 0.0 samples = 1 value = -0.976 332->333 334 dcoilwtico ≤ 1.074 mae = 0.009 samples = 2 value = -0.936 332->334 335 mae = 0.0 samples = 1 value = -0.945 334->335 336 mae = 0.0 samples = 1 value = -0.927 334->336 338 mae = 0.0 samples = 1 value = -1.067 337->338 339 mae = 0.0 samples = 1 value = -0.986 337->339 341 mae = 0.0 samples = 1 value = -1.354 340->341 342 mae = 0.0 samples = 1 value = -1.325 340->342 344 month_09 ≤ 0.5 mae = 0.178 samples = 4 value = -0.754 343->344 351 mae = 0.0 samples = 1 value = -0.517 343->351 345 dcoilwtico ≤ 1.123 mae = 0.033 samples = 3 value = -0.705 344->345 350 mae = 0.0 samples = 1 value = -1.319 344->350 346 mae = 0.0 samples = 1 value = -0.704 345->346 347 month_08 ≤ 0.5 mae = 0.049 samples = 2 value = -0.754 345->347 348 mae = 0.0 samples = 1 value = -0.803 347->348 349 mae = 0.0 samples = 1 value = -0.705 347->349 353 dcoilwtico ≤ 0.685 mae = 0.036 samples = 3 value = -1.254 352->353 358 dcoilwtico ≤ 0.811 mae = 0.105 samples = 6 value = -1.071 352->358 354 mae = 0.0 samples = 1 value = -1.297 353->354 355 day_Thursday ≤ 0.5 mae = 0.033 samples = 2 value = -1.221 353->355 356 mae = 0.0 samples = 1 value = -1.254 355->356 357 mae = 0.0 samples = 1 value = -1.187 355->357 359 mae = 0.0 samples = 1 value = -1.142 358->359 360 dcoilwtico ≤ 0.89 mae = 0.11 samples = 5 value = -1.063 358->360 361 mae = 0.0 samples = 1 value = -1.079 360->361 362 dcoilwtico ≤ 0.939 mae = 0.133 samples = 4 value = -1.02 360->362 363 mae = 0.0 samples = 1 value = -0.764 362->363 364 dcoilwtico ≤ 0.964 mae = 0.078 samples = 3 value = -1.063 362->364 365 mae = 0.0 samples = 1 value = -1.211 364->365 366 dcoilwtico ≤ 0.973 mae = 0.043 samples = 2 value = -1.02 364->366 367 mae = 0.0 samples = 1 value = -0.978 366->367 368 mae = 0.0 samples = 1 value = -1.063 366->368 370 locale_name_Ecuador ≤ 0.5 mae = 0.254 samples = 31 value = -0.532 369->370 431 dcoilwtico ≤ 0.674 mae = 0.253 samples = 61 value = -0.685 369->431 371 month_08 ≤ 0.5 mae = 0.216 samples = 28 value = -0.551 370->371 426 dcoilwtico ≤ -1.431 mae = 0.208 samples = 3 value = 0.144 370->426 372 dcoilwtico ≤ -0.841 mae = 0.212 samples = 25 value = -0.59 371->372 421 dcoilwtico ≤ -1.512 mae = 0.113 samples = 3 value = -0.429 371->421 373 month_06 ≤ 0.5 mae = 0.201 samples = 22 value = -0.603 372->373 416 dcoilwtico ≤ -0.825 mae = 0.165 samples = 3 value = -0.451 372->416 374 month_09 ≤ 0.5 mae = 0.203 samples = 20 value = -0.591 373->374 413 dcoilwtico ≤ -0.903 mae = 0.015 samples = 2 value = -0.772 373->413 375 month_01 ≤ 0.5 mae = 0.197 samples = 16 value = -0.578 374->375 406 dcoilwtico ≤ -1.399 mae = 0.152 samples = 4 value = -0.753 374->406 376 month_03 ≤ 0.5 mae = 0.209 samples = 14 value = -0.591 375->376 403 dcoilwtico ≤ -1.385 mae = 0.013 samples = 2 value = -0.474 375->403 377 dcoilwtico ≤ -1.132 mae = 0.197 samples = 12 value = -0.603 376->377 400 dcoilwtico ≤ -1.372 mae = 0.19 samples = 2 value = -0.313 376->400 378 dcoilwtico ≤ -1.153 mae = 0.227 samples = 9 value = -0.657 377->378 395 month_02 ≤ 0.5 mae = 0.036 samples = 3 value = -0.536 377->395 379 dcoilwtico ≤ -1.545 mae = 0.111 samples = 8 value = -0.636 378->379 394 mae = 0.0 samples = 1 value = -1.812 378->394 380 dcoilwtico ≤ -1.641 mae = 0.012 samples = 2 value = -0.602 379->380 383 month_07 ≤ 0.5 mae = 0.13 samples = 6 value = -0.667 379->383 381 mae = 0.0 samples = 1 value = -0.614 380->381 382 mae = 0.0 samples = 1 value = -0.59 380->382 384 dcoilwtico ≤ -1.458 mae = 0.138 samples = 5 value = -0.677 383->384 393 mae = 0.0 samples = 1 value = -0.592 383->393 385 mae = 0.0 samples = 1 value = -0.706 384->385 386 dcoilwtico ≤ -1.27 mae = 0.166 samples = 4 value = -0.667 384->386 387 month_02 ≤ 0.5 mae = 0.063 samples = 2 value = -0.594 386->387 390 month_02 ≤ 0.5 mae = 0.248 samples = 2 value = -0.925 386->390 388 mae = 0.0 samples = 1 value = -0.657 387->388 389 mae = 0.0 samples = 1 value = -0.532 387->389 391 mae = 0.0 samples = 1 value = -0.677 390->391 392 mae = 0.0 samples = 1 value = -1.174 390->392 396 dcoilwtico ≤ -0.934 mae = 0.015 samples = 2 value = -0.551 395->396 399 mae = 0.0 samples = 1 value = -0.459 395->399 397 mae = 0.0 samples = 1 value = -0.566 396->397 398 mae = 0.0 samples = 1 value = -0.536 396->398 401 mae = 0.0 samples = 1 value = -0.504 400->401 402 mae = 0.0 samples = 1 value = -0.123 400->402 404 mae = 0.0 samples = 1 value = -0.461 403->404 405 mae = 0.0 samples = 1 value = -0.488 403->405 407 dcoilwtico ≤ -1.446 mae = 0.126 samples = 3 value = -0.744 406->407 412 mae = 0.0 samples = 1 value = -0.974 406->412 408 locale_name_Ibarra ≤ 0.5 mae = 0.009 samples = 2 value = -0.753 407->408 411 mae = 0.0 samples = 1 value = -0.386 407->411 409 mae = 0.0 samples = 1 value = -0.763 408->409 410 mae = 0.0 samples = 1 value = -0.744 408->410 414 mae = 0.0 samples = 1 value = -0.757 413->414 415 mae = 0.0 samples = 1 value = -0.787 413->415 417 mae = 0.0 samples = 1 value = -0.474 416->417 418 dcoilwtico ≤ -0.5 mae = 0.236 samples = 2 value = -0.216 416->418 419 mae = 0.0 samples = 1 value = 0.02 418->419 420 mae = 0.0 samples = 1 value = -0.451 418->420 422 transferred_False ≤ 0.5 mae = 0.003 samples = 2 value = -0.432 421->422 425 mae = 0.0 samples = 1 value = -0.096 421->425 423 mae = 0.0 samples = 1 value = -0.429 422->423 424 mae = 0.0 samples = 1 value = -0.435 422->424 427 month_08 ≤ 0.5 mae = 0.102 samples = 2 value = 0.246 426->427 430 mae = 0.0 samples = 1 value = -0.277 426->430 428 mae = 0.0 samples = 1 value = 0.144 427->428 429 mae = 0.0 samples = 1 value = 0.348 427->429 432 month_09 ≤ 0.5 mae = 0.307 samples = 25 value = -0.804 431->432 481 month_10 ≤ 0.5 mae = 0.199 samples = 36 value = -0.659 431->481 433 dcoilwtico ≤ 0.564 mae = 0.214 samples = 22 value = -0.891 432->433 476 dcoilwtico ≤ 0.562 mae = 0.455 samples = 3 value = -0.07 432->476 434 dcoilwtico ≤ 0.544 mae = 0.212 samples = 12 value = -0.716 433->434 457 locale_Local ≤ 0.5 mae = 0.138 samples = 10 value = -1.008 433->457 435 month_10 ≤ 0.5 mae = 0.195 samples = 10 value = -0.781 434->435 454 dcoilwtico ≤ 0.552 mae = 0.078 samples = 2 value = -0.467 434->454 436 month_01 ≤ 0.5 mae = 0.157 samples = 7 value = -0.896 435->436 449 dcoilwtico ≤ 0.084 mae = 0.162 samples = 3 value = -0.608 435->449 437 month_04 ≤ 0.5 mae = 0.125 samples = 6 value = -0.915 436->437 448 mae = 0.0 samples = 1 value = -0.55 436->448 438 month_02 ≤ 0.5 mae = 0.109 samples = 5 value = -0.896 437->438 447 mae = 0.0 samples = 1 value = -1.103 437->447 439 locale_name_Guaranda ≤ 0.5 mae = 0.09 samples = 4 value = -0.848 438->439 446 mae = 0.0 samples = 1 value = -1.082 438->446 440 dcoilwtico ≤ 0.462 mae = 0.088 samples = 3 value = -0.8 439->440 445 mae = 0.0 samples = 1 value = -0.896 439->445 441 mae = 0.0 samples = 1 value = -0.67 440->441 442 dcoilwtico ≤ 0.522 mae = 0.067 samples = 2 value = -0.867 440->442 443 mae = 0.0 samples = 1 value = -0.933 442->443 444 mae = 0.0 samples = 1 value = -0.8 442->444 450 mae = 0.0 samples = 1 value = -0.763 449->450 451 dcoilwtico ≤ 0.273 mae = 0.166 samples = 2 value = -0.441 449->451 452 mae = 0.0 samples = 1 value = -0.275 451->452 453 mae = 0.0 samples = 1 value = -0.608 451->453 455 mae = 0.0 samples = 1 value = -0.545 454->455 456 mae = 0.0 samples = 1 value = -0.389 454->456 458 locale_National ≤ 0.5 mae = 0.118 samples = 9 value = -1.027 457->458 475 mae = 0.0 samples = 1 value = -0.717 457->475 459 dcoilwtico ≤ 0.66 mae = 0.11 samples = 8 value = -1.008 458->459 474 mae = 0.0 samples = 1 value = -1.215 458->474 460 month_01 ≤ 0.5 mae = 0.087 samples = 6 value = -0.979 459->460 471 dcoilwtico ≤ 0.664 mae = 0.077 samples = 2 value = -1.168 459->471 461 dcoilwtico ≤ 0.607 mae = 0.058 samples = 4 value = -1.008 460->461 468 dcoilwtico ≤ 0.628 mae = 0.041 samples = 2 value = -0.845 460->468 462 month_03 ≤ 0.5 mae = 0.067 samples = 2 value = -1.094 461->462 465 month_05 ≤ 0.5 mae = 0.011 samples = 2 value = -0.979 461->465 463 mae = 0.0 samples = 1 value = -1.162 462->463 464 mae = 0.0 samples = 1 value = -1.027 462->464 466 mae = 0.0 samples = 1 value = -0.99 465->466 467 mae = 0.0 samples = 1 value = -0.968 465->467 469 mae = 0.0 samples = 1 value = -0.887 468->469 470 mae = 0.0 samples = 1 value = -0.804 468->470 472 mae = 0.0 samples = 1 value = -1.246 471->472 473 mae = 0.0 samples = 1 value = -1.091 471->473 477 dcoilwtico ≤ 0.506 mae = 0.194 samples = 2 value = -0.263 476->477 480 mae = 0.0 samples = 1 value = 0.909 476->480 478 mae = 0.0 samples = 1 value = -0.457 477->478 479 mae = 0.0 samples = 1 value = -0.07 477->479 482 dcoilwtico ≤ 1.086 mae = 0.179 samples = 33 value = -0.641 481->482 547 transferred_False ≤ 0.5 mae = 0.142 samples = 3 value = -1.015 481->547 483 month_08 ≤ 0.5 mae = 0.194 samples = 27 value = -0.556 482->483 536 dcoilwtico ≤ 1.104 mae = 0.029 samples = 6 value = -0.7 482->536 484 dcoilwtico ≤ 0.893 mae = 0.164 samples = 25 value = -0.592 483->484 533 dcoilwtico ≤ 0.721 mae = 0.073 samples = 2 value = 0.003 483->533 485 dcoilwtico ≤ 0.685 mae = 0.106 samples = 9 value = -0.71 484->485 502 month_02 ≤ 0.5 mae = 0.163 samples = 16 value = -0.501 484->502 486 mae = 0.0 samples = 1 value = -0.449 485->486 487 dcoilwtico ≤ 0.699 mae = 0.086 samples = 8 value = -0.713 485->487 488 mae = 0.0 samples = 1 value = -0.987 487->488 489 dcoilwtico ≤ 0.828 mae = 0.059 samples = 7 value = -0.71 487->489 490 month_03 ≤ 0.5 mae = 0.081 samples = 4 value = -0.638 489->490 497 month_03 ≤ 0.5 mae = 0.008 samples = 3 value = -0.717 489->497 491 month_05 ≤ 0.5 mae = 0.04 samples = 3 value = -0.685 490->491 496 mae = 0.0 samples = 1 value = -0.481 490->496 492 locale_None ≤ 0.5 mae = 0.013 samples = 2 value = -0.698 491->492 495 mae = 0.0 samples = 1 value = -0.592 491->495 493 mae = 0.0 samples = 1 value = -0.685 492->493 494 mae = 0.0 samples = 1 value = -0.71 492->494 498 locale_name_Puyo ≤ 0.5 mae = 0.001 samples = 2 value = -0.716 497->498 501 mae = 0.0 samples = 1 value = -0.74 497->501 499 mae = 0.0 samples = 1 value = -0.717 498->499 500 mae = 0.0 samples = 1 value = -0.716 498->500 503 dcoilwtico ≤ 1.048 mae = 0.155 samples = 14 value = -0.471 502->503 530 dcoilwtico ≤ 0.938 mae = 0.069 samples = 2 value = -0.71 502->530 504 month_03 ≤ 0.5 mae = 0.161 samples = 11 value = -0.43 503->504 525 dcoilwtico ≤ 1.06 mae = 0.049 samples = 3 value = -0.556 503->525 505 dcoilwtico ≤ 0.958 mae = 0.162 samples = 10 value = -0.443 504->505 524 mae = 0.0 samples = 1 value = -0.273 504->524 506 dcoilwtico ≤ 0.956 mae = 0.124 samples = 3 value = -0.594 505->506 511 dcoilwtico ≤ 0.979 mae = 0.154 samples = 7 value = -0.422 505->511 507 month_05 ≤ 0.5 mae = 0.082 samples = 2 value = -0.512 506->507 510 mae = 0.0 samples = 1 value = -0.801 506->510 508 mae = 0.0 samples = 1 value = -0.43 507->508 509 mae = 0.0 samples = 1 value = -0.594 507->509 512 mae = 0.0 samples = 1 value = -0.108 511->512 513 dcoilwtico ≤ 1.009 mae = 0.127 samples = 6 value = -0.439 511->513 514 type_No_Holiday ≤ 0.5 mae = 0.003 samples = 2 value = -0.419 513->514 517 month_07 ≤ 0.5 mae = 0.172 samples = 4 value = -0.471 513->517 515 mae = 0.0 samples = 1 value = -0.422 514->515 516 mae = 0.0 samples = 1 value = -0.417 514->516 518 dcoilwtico ≤ 1.038 mae = 0.023 samples = 3 value = -0.455 517->518 523 mae = 0.0 samples = 1 value = -1.075 517->523 519 transferred_False ≤ 0.5 mae = 0.016 samples = 2 value = -0.471 518->519 522 mae = 0.0 samples = 1 value = -0.418 518->522 520 mae = 0.0 samples = 1 value = -0.488 519->520 521 mae = 0.0 samples = 1 value = -0.455 519->521 526 mae = 0.0 samples = 1 value = -0.514 525->526 527 type_No_Holiday ≤ 0.5 mae = 0.053 samples = 2 value = -0.608 525->527 528 mae = 0.0 samples = 1 value = -0.556 527->528 529 mae = 0.0 samples = 1 value = -0.661 527->529 531 mae = 0.0 samples = 1 value = -0.78 530->531 532 mae = 0.0 samples = 1 value = -0.641 530->532 534 mae = 0.0 samples = 1 value = 0.076 533->534 535 mae = 0.0 samples = 1 value = -0.071 533->535 537 month_07 ≤ 0.5 mae = 0.001 samples = 2 value = -0.682 536->537 540 type_No_Holiday ≤ 0.5 mae = 0.027 samples = 4 value = -0.727 536->540 538 mae = 0.0 samples = 1 value = -0.684 537->538 539 mae = 0.0 samples = 1 value = -0.681 537->539 541 mae = 0.0 samples = 1 value = -0.657 540->541 542 month_09 ≤ 0.5 mae = 0.01 samples = 3 value = -0.737 540->542 543 month_07 ≤ 0.5 mae = 0.004 samples = 2 value = -0.741 542->543 546 mae = 0.0 samples = 1 value = -0.716 542->546 544 mae = 0.0 samples = 1 value = -0.745 543->544 545 mae = 0.0 samples = 1 value = -0.737 543->545 548 dcoilwtico ≤ 0.868 mae = 0.147 samples = 2 value = -1.162 547->548 551 mae = 0.0 samples = 1 value = -0.884 547->551 549 mae = 0.0 samples = 1 value = -1.015 548->549 550 mae = 0.0 samples = 1 value = -1.309 548->550 553 locale_National ≤ 0.5 mae = 0.204 samples = 40 value = -0.157 552->553 632 dcoilwtico ≤ 0.754 mae = 0.209 samples = 41 value = -0.377 552->632 554 locale_Regional ≤ 0.5 mae = 0.18 samples = 39 value = -0.163 553->554 631 mae = 0.0 samples = 1 value = 0.972 553->631 555 month_04 ≤ 0.5 mae = 0.173 samples = 38 value = -0.17 554->555 630 mae = 0.0 samples = 1 value = 0.284 554->630 556 month_05 ≤ 0.5 mae = 0.172 samples = 33 value = -0.147 555->556 621 dcoilwtico ≤ -0.354 mae = 0.124 samples = 5 value = -0.306 555->621 557 dcoilwtico ≤ -0.171 mae = 0.166 samples = 31 value = -0.146 556->557 618 dcoilwtico ≤ -0.871 mae = 0.142 samples = 2 value = -0.416 556->618 558 month_10 ≤ 0.5 mae = 0.171 samples = 26 value = -0.149 557->558 609 month_09 ≤ 0.5 mae = 0.104 samples = 5 value = -0.092 557->609 559 dcoilwtico ≤ -0.528 mae = 0.164 samples = 24 value = -0.146 558->559 606 dcoilwtico ≤ -1.415 mae = 0.058 samples = 2 value = -0.411 558->606 560 month_02 ≤ 0.5 mae = 0.162 samples = 23 value = -0.146 559->560 605 mae = 0.0 samples = 1 value = -0.351 559->605 561 dcoilwtico ≤ -1.676 mae = 0.166 samples = 20 value = -0.144 560->561 600 dcoilwtico ≤ -1.272 mae = 0.093 samples = 3 value = -0.27 560->600 562 mae = 0.0 samples = 1 value = -0.262 561->562 563 month_03 ≤ 0.5 mae = 0.168 samples = 19 value = -0.142 561->563 564 dcoilwtico ≤ -1.382 mae = 0.174 samples = 18 value = -0.142 563->564 599 mae = 0.0 samples = 1 value = -0.204 563->599 565 dcoilwtico ≤ -1.423 mae = 0.165 samples = 9 value = -0.151 564->565 582 month_01 ≤ 0.5 mae = 0.181 samples = 9 value = -0.138 564->582 566 month_01 ≤ 0.5 mae = 0.159 samples = 8 value = -0.149 565->566 581 mae = 0.0 samples = 1 value = -0.362 565->581 567 locale_name_Latacunga ≤ 0.5 mae = 0.157 samples = 7 value = -0.147 566->567 580 mae = 0.0 samples = 1 value = -0.325 566->580 568 dcoilwtico ≤ -1.62 mae = 0.178 samples = 6 value = -0.149 567->568 579 mae = 0.0 samples = 1 value = -0.118 567->579 569 month_11 ≤ 0.5 mae = 0.017 samples = 2 value = -0.168 568->569 572 month_11 ≤ 0.5 mae = 0.256 samples = 4 value = -0.144 568->572 570 mae = 0.0 samples = 1 value = -0.184 569->570 571 mae = 0.0 samples = 1 value = -0.151 569->571 573 dcoilwtico ≤ -1.47 mae = 0.122 samples = 3 value = -0.147 572->573 578 mae = 0.0 samples = 1 value = 0.513 572->578 574 mae = 0.0 samples = 1 value = -0.142 573->574 575 locale_Local ≤ 0.5 mae = 0.18 samples = 2 value = -0.327 573->575 576 mae = 0.0 samples = 1 value = -0.508 575->576 577 mae = 0.0 samples = 1 value = -0.147 575->577 583 dcoilwtico ≤ -1.331 mae = 0.128 samples = 8 value = -0.14 582->583 598 mae = 0.0 samples = 1 value = 0.466 582->598 584 mae = 0.0 samples = 1 value = -0.115 583->584 585 dcoilwtico ≤ -1.286 mae = 0.142 samples = 7 value = -0.142 583->585 586 mae = 0.0 samples = 1 value = -0.367 585->586 587 dcoilwtico ≤ -1.231 mae = 0.129 samples = 6 value = -0.14 585->587 588 mae = 0.0 samples = 1 value = -0.146 587->588 589 month_06 ≤ 0.5 mae = 0.153 samples = 5 value = -0.138 587->589 590 dcoilwtico ≤ -1.183 mae = 0.152 samples = 3 value = 0.167 589->590 595 dcoilwtico ≤ -0.832 mae = 0.002 samples = 2 value = -0.14 589->595 591 mae = 0.0 samples = 1 value = -0.142 590->591 592 dcoilwtico ≤ -1.068 mae = 0.073 samples = 2 value = 0.24 590->592 593 mae = 0.0 samples = 1 value = 0.314 592->593 594 mae = 0.0 samples = 1 value = 0.167 592->594 596 mae = 0.0 samples = 1 value = -0.142 595->596 597 mae = 0.0 samples = 1 value = -0.138 595->597 601 mae = 0.0 samples = 1 value = -0.067 600->601 602 dcoilwtico ≤ -1.196 mae = 0.038 samples = 2 value = -0.308 600->602 603 mae = 0.0 samples = 1 value = -0.346 602->603 604 mae = 0.0 samples = 1 value = -0.27 602->604 607 mae = 0.0 samples = 1 value = -0.353 606->607 608 mae = 0.0 samples = 1 value = -0.469 606->608 610 month_11 ≤ 0.5 mae = 0.033 samples = 4 value = -0.094 609->610 617 mae = 0.0 samples = 1 value = 0.295 609->617 611 dcoilwtico ≤ 0.199 mae = 0.024 samples = 3 value = -0.095 610->611 616 mae = 0.0 samples = 1 value = -0.034 610->616 612 mae = 0.0 samples = 1 value = -0.092 611->612 613 month_10 ≤ 0.5 mae = 0.034 samples = 2 value = -0.129 611->613 614 mae = 0.0 samples = 1 value = -0.095 613->614 615 mae = 0.0 samples = 1 value = -0.163 613->615 619 mae = 0.0 samples = 1 value = -0.274 618->619 620 mae = 0.0 samples = 1 value = -0.558 618->620 622 dcoilwtico ≤ -1.108 mae = 0.043 samples = 3 value = -0.196 621->622 627 dcoilwtico ≤ 0.369 mae = 0.003 samples = 2 value = -0.498 621->627 623 mae = 0.0 samples = 1 value = -0.306 622->623 624 dcoilwtico ≤ -0.987 mae = 0.009 samples = 2 value = -0.187 622->624 625 mae = 0.0 samples = 1 value = -0.196 624->625 626 mae = 0.0 samples = 1 value = -0.178 624->626 628 mae = 0.0 samples = 1 value = -0.496 627->628 629 mae = 0.0 samples = 1 value = -0.501 627->629 633 month_08 ≤ 0.5 mae = 0.215 samples = 15 value = -0.496 632->633 662 dcoilwtico ≤ 1.161 mae = 0.175 samples = 26 value = -0.296 632->662 634 dcoilwtico ≤ 0.693 mae = 0.131 samples = 13 value = -0.513 633->634 659 dcoilwtico ≤ 0.693 mae = 0.232 samples = 2 value = 0.261 633->659 635 dcoilwtico ≤ 0.56 mae = 0.119 samples = 9 value = -0.475 634->635 652 month_02 ≤ 0.5 mae = 0.027 samples = 4 value = -0.652 634->652 636 mae = 0.0 samples = 1 value = -0.832 635->636 637 month_05 ≤ 0.5 mae = 0.089 samples = 8 value = -0.475 635->637 638 dcoilwtico ≤ 0.576 mae = 0.048 samples = 5 value = -0.496 637->638 647 dcoilwtico ≤ 0.628 mae = 0.09 samples = 3 value = -0.294 637->647 639 mae = 0.0 samples = 1 value = -0.605 638->639 640 locale_None ≤ 0.5 mae = 0.033 samples = 4 value = -0.485 638->640 641 mae = 0.0 samples = 1 value = -0.403 640->641 642 month_11 ≤ 0.5 mae = 0.013 samples = 3 value = -0.496 640->642 643 month_01 ≤ 0.5 mae = 0.009 samples = 2 value = -0.504 642->643 646 mae = 0.0 samples = 1 value = -0.474 642->646 644 mae = 0.0 samples = 1 value = -0.513 643->644 645 mae = 0.0 samples = 1 value = -0.496 643->645 648 dcoilwtico ≤ 0.581 mae = 0.091 samples = 2 value = -0.385 647->648 651 mae = 0.0 samples = 1 value = -0.204 647->651 649 mae = 0.0 samples = 1 value = -0.294 648->649 650 mae = 0.0 samples = 1 value = -0.475 648->650 653 dcoilwtico ≤ 0.734 mae = 0.019 samples = 3 value = -0.672 652->653 658 mae = 0.0 samples = 1 value = -0.621 652->658 654 month_01 ≤ 0.5 mae = 0.008 samples = 2 value = -0.68 653->654 657 mae = 0.0 samples = 1 value = -0.632 653->657 655 mae = 0.0 samples = 1 value = -0.672 654->655 656 mae = 0.0 samples = 1 value = -0.688 654->656 660 mae = 0.0 samples = 1 value = 0.493 659->660 661 mae = 0.0 samples = 1 value = 0.029 659->661 663 dcoilwtico ≤ 1.051 mae = 0.164 samples = 25 value = -0.279 662->663 712 mae = 0.0 samples = 1 value = -0.731 662->712 664 dcoilwtico ≤ 1.044 mae = 0.145 samples = 20 value = -0.26 663->664 703 month_07 ≤ 0.5 mae = 0.172 samples = 5 value = -0.387 663->703 665 month_02 ≤ 0.5 mae = 0.127 samples = 19 value = -0.263 664->665 702 mae = 0.0 samples = 1 value = 0.225 664->702 666 month_10 ≤ 0.5 mae = 0.123 samples = 17 value = -0.257 665->666 699 dcoilwtico ≤ 0.964 mae = 0.019 samples = 2 value = -0.421 665->699 667 month_08 ≤ 0.5 mae = 0.114 samples = 14 value = -0.232 666->667 694 dcoilwtico ≤ 0.966 mae = 0.069 samples = 3 value = -0.475 666->694 668 dcoilwtico ≤ 0.962 mae = 0.098 samples = 12 value = -0.195 667->668 691 dcoilwtico ≤ 1.005 mae = 0.048 samples = 2 value = -0.425 667->691 669 month_04 ≤ 0.5 mae = 0.099 samples = 8 value = -0.232 668->669 684 dcoilwtico ≤ 0.98 mae = 0.055 samples = 4 value = -0.129 668->684 670 type_No_Holiday ≤ 0.5 mae = 0.095 samples = 7 value = -0.249 669->670 683 mae = 0.0 samples = 1 value = -0.118 669->683 671 mae = 0.0 samples = 1 value = -0.323 670->671 672 dcoilwtico ≤ 0.868 mae = 0.098 samples = 6 value = -0.232 670->672 673 dcoilwtico ≤ 0.811 mae = 0.004 samples = 2 value = -0.253 672->673 676 dcoilwtico ≤ 0.892 mae = 0.129 samples = 4 value = -0.195 672->676 674 mae = 0.0 samples = 1 value = -0.249 673->674 675 mae = 0.0 samples = 1 value = -0.257 673->675 677 mae = 0.0 samples = 1 value = 0.209 676->677 678 month_07 ≤ 0.5 mae = 0.03 samples = 3 value = -0.216 676->678 679 mae = 0.0 samples = 1 value = -0.263 678->679 680 dcoilwtico ≤ 0.91 mae = 0.021 samples = 2 value = -0.195 678->680 681 mae = 0.0 samples = 1 value = -0.174 680->681 682 mae = 0.0 samples = 1 value = -0.216 680->682 685 mae = 0.0 samples = 1 value = -0.1 684->685 686 month_05 ≤ 0.5 mae = 0.063 samples = 3 value = -0.134 684->686 687 transferred_None ≤ 0.5 mae = 0.005 samples = 2 value = -0.129 686->687 690 mae = 0.0 samples = 1 value = -0.313 686->690 688 mae = 0.0 samples = 1 value = -0.134 687->688 689 mae = 0.0 samples = 1 value = -0.124 687->689 692 mae = 0.0 samples = 1 value = -0.377 691->692 693 mae = 0.0 samples = 1 value = -0.473 691->693 695 locale_None ≤ 0.5 mae = 0.006 samples = 2 value = -0.482 694->695 698 mae = 0.0 samples = 1 value = -0.279 694->698 696 mae = 0.0 samples = 1 value = -0.475 695->696 697 mae = 0.0 samples = 1 value = -0.488 695->697 700 mae = 0.0 samples = 1 value = -0.439 699->700 701 mae = 0.0 samples = 1 value = -0.402 699->701 704 dcoilwtico ≤ 1.142 mae = 0.15 samples = 3 value = -0.375 703->704 709 dcoilwtico ≤ 1.076 mae = 0.005 samples = 2 value = -0.587 703->709 705 month_06 ≤ 0.5 mae = 0.219 samples = 2 value = -0.156 704->705 708 mae = 0.0 samples = 1 value = -0.387 704->708 706 mae = 0.0 samples = 1 value = 0.063 705->706 707 mae = 0.0 samples = 1 value = -0.375 705->707 710 mae = 0.0 samples = 1 value = -0.592 709->710 711 mae = 0.0 samples = 1 value = -0.582 709->711 714 locale_National ≤ 0.5 mae = 0.255 samples = 40 value = -0.06 713->714 793 type_Transfer ≤ 0.5 mae = 0.299 samples = 56 value = -0.343 713->793 715 month_08 ≤ 0.5 mae = 0.206 samples = 34 value = -0.117 714->715 782 month_05 ≤ 0.5 mae = 0.256 samples = 6 value = 0.45 714->782 716 month_07 ≤ 0.5 mae = 0.199 samples = 31 value = -0.131 715->716 777 dcoilwtico ≤ -1.529 mae = 0.035 samples = 3 value = 0.169 715->777 717 dcoilwtico ≤ 0.262 mae = 0.177 samples = 27 value = -0.175 716->717 770 dcoilwtico ≤ -1.068 mae = 0.155 samples = 4 value = 0.114 716->770 718 month_02 ≤ 0.5 mae = 0.157 samples = 26 value = -0.187 717->718 769 mae = 0.0 samples = 1 value = 0.523 717->769 719 dcoilwtico ≤ -1.565 mae = 0.155 samples = 24 value = -0.172 718->719 766 dcoilwtico ≤ -1.212 mae = 0.019 samples = 2 value = -0.357 718->766 720 dcoilwtico ≤ -1.659 mae = 0.045 samples = 2 value = 0.005 719->720 723 month_05 ≤ 0.5 mae = 0.153 samples = 22 value = -0.187 719->723 721 mae = 0.0 samples = 1 value = 0.049 720->721 722 mae = 0.0 samples = 1 value = -0.04 720->722 724 dcoilwtico ≤ 0.085 mae = 0.124 samples = 19 value = -0.207 723->724 761 dcoilwtico ≤ -0.865 mae = 0.26 samples = 3 value = -0.116 723->761 725 month_06 ≤ 0.5 mae = 0.119 samples = 18 value = -0.222 724->725 760 mae = 0.0 samples = 1 value = 0.005 724->760 726 month_01 ≤ 0.5 mae = 0.115 samples = 17 value = -0.207 725->726 759 mae = 0.0 samples = 1 value = -0.401 725->759 727 dcoilwtico ≤ 0.038 mae = 0.096 samples = 14 value = -0.203 726->727 754 dcoilwtico ≤ -1.311 mae = 0.162 samples = 3 value = -0.335 726->754 728 dcoilwtico ≤ -1.461 mae = 0.095 samples = 13 value = -0.199 727->728 753 mae = 0.0 samples = 1 value = -0.303 727->753 729 month_09 ≤ 0.5 mae = 0.02 samples = 3 value = -0.236 728->729 734 month_09 ≤ 0.5 mae = 0.107 samples = 10 value = -0.172 728->734 730 mae = 0.0 samples = 1 value = -0.267 729->730 731 dcoilwtico ≤ -1.464 mae = 0.015 samples = 2 value = -0.222 729->731 732 mae = 0.0 samples = 1 value = -0.207 731->732 733 mae = 0.0 samples = 1 value = -0.236 731->733 735 dcoilwtico ≤ -1.087 mae = 0.079 samples = 9 value = -0.175 734->735 752 mae = 0.0 samples = 1 value = 0.187 734->752 736 month_10 ≤ 0.5 mae = 0.07 samples = 6 value = -0.144 735->736 747 dcoilwtico ≤ -0.525 mae = 0.07 samples = 3 value = -0.249 735->747 737 dcoilwtico ≤ -1.435 mae = 0.049 samples = 5 value = -0.17 736->737 746 mae = 0.0 samples = 1 value = 0.003 736->746 738 mae = 0.0 samples = 1 value = -0.118 737->738 739 dcoilwtico ≤ -1.278 mae = 0.048 samples = 4 value = -0.184 737->739 740 dcoilwtico ≤ -1.353 mae = 0.02 samples = 2 value = -0.219 739->740 743 month_03 ≤ 0.5 mae = 0.047 samples = 2 value = -0.122 739->743 741 mae = 0.0 samples = 1 value = -0.199 740->741 742 mae = 0.0 samples = 1 value = -0.239 740->742 744 mae = 0.0 samples = 1 value = -0.17 743->744 745 mae = 0.0 samples = 1 value = -0.075 743->745 748 mae = 0.0 samples = 1 value = -0.386 747->748 749 transferred_False ≤ 0.5 mae = 0.037 samples = 2 value = -0.212 747->749 750 mae = 0.0 samples = 1 value = -0.249 749->750 751 mae = 0.0 samples = 1 value = -0.175 749->751 755 dcoilwtico ≤ -1.378 mae = 0.112 samples = 2 value = -0.223 754->755 758 mae = 0.0 samples = 1 value = -0.596 754->758 756 mae = 0.0 samples = 1 value = -0.335 755->756 757 mae = 0.0 samples = 1 value = -0.111 755->757 762 mae = 0.0 samples = 1 value = -0.131 761->762 763 dcoilwtico ≤ -0.837 mae = 0.383 samples = 2 value = 0.267 761->763 764 mae = 0.0 samples = 1 value = 0.65 763->764 765 mae = 0.0 samples = 1 value = -0.116 763->765 767 mae = 0.0 samples = 1 value = -0.338 766->767 768 mae = 0.0 samples = 1 value = -0.375 766->768 771 dcoilwtico ≤ -1.17 mae = 0.057 samples = 3 value = 0.091 770->771 776 mae = 0.0 samples = 1 value = 0.537 770->776 772 dcoilwtico ≤ -1.287 mae = 0.023 samples = 2 value = 0.114 771->772 775 mae = 0.0 samples = 1 value = -0.035 771->775 773 mae = 0.0 samples = 1 value = 0.091 772->773 774 mae = 0.0 samples = 1 value = 0.137 772->774 778 mae = 0.0 samples = 1 value = 0.197 777->778 779 dcoilwtico ≤ -1.47 mae = 0.039 samples = 2 value = 0.131 777->779 780 mae = 0.0 samples = 1 value = 0.092 779->780 781 mae = 0.0 samples = 1 value = 0.169 779->781 783 dcoilwtico ≤ -0.858 mae = 0.191 samples = 5 value = 0.395 782->783 792 mae = 0.0 samples = 1 value = 0.973 782->792 784 month_04 ≤ 0.5 mae = 0.129 samples = 4 value = 0.45 783->784 791 mae = 0.0 samples = 1 value = -0.046 783->791 785 type_Bridge ≤ 0.5 mae = 0.065 samples = 3 value = 0.506 784->785 790 mae = 0.0 samples = 1 value = 0.185 784->790 786 type_Holiday ≤ 0.5 mae = 0.043 samples = 2 value = 0.549 785->786 789 mae = 0.0 samples = 1 value = 0.395 785->789 787 mae = 0.0 samples = 1 value = 0.506 786->787 788 mae = 0.0 samples = 1 value = 0.591 786->788 794 month_05 ≤ 0.5 mae = 0.262 samples = 55 value = -0.338 793->794 901 mae = 0.0 samples = 1 value = -2.66 793->901 795 dcoilwtico ≤ 1.163 mae = 0.257 samples = 52 value = -0.351 794->795 896 dcoilwtico ≤ 0.881 mae = 0.054 samples = 3 value = -0.012 794->896 796 month_10 ≤ 0.5 mae = 0.252 samples = 49 value = -0.338 795->796 891 dcoilwtico ≤ 1.224 mae = 0.049 samples = 3 value = -0.646 795->891 797 dcoilwtico ≤ 1.025 mae = 0.246 samples = 47 value = -0.335 796->797 888 dcoilwtico ≤ 0.795 mae = 0.021 samples = 2 value = -0.729 796->888 798 dcoilwtico ≤ 0.728 mae = 0.251 samples = 37 value = -0.31 797->798 869 locale_name_Ecuador ≤ 0.5 mae = 0.2 samples = 10 value = -0.445 797->869 799 month_09 ≤ 0.5 mae = 0.235 samples = 20 value = -0.387 798->799 836 type_Holiday ≤ 0.5 mae = 0.219 samples = 17 value = -0.182 798->836 800 dcoilwtico ≤ 0.574 mae = 0.181 samples = 17 value = -0.473 799->800 831 dcoilwtico ≤ 0.6 mae = 0.222 samples = 3 value = 0.323 799->831 801 transferred_None ≤ 0.5 mae = 0.115 samples = 8 value = -0.343 800->801 816 month_11 ≤ 0.5 mae = 0.165 samples = 9 value = -0.613 800->816 802 mae = 0.0 samples = 1 value = -0.53 801->802 803 month_01 ≤ 0.5 mae = 0.104 samples = 7 value = -0.334 801->803 804 dcoilwtico ≤ 0.566 mae = 0.098 samples = 4 value = -0.343 803->804 811 dcoilwtico ≤ 0.53 mae = 0.071 samples = 3 value = -0.211 803->811 805 dcoilwtico ≤ 0.54 mae = 0.111 samples = 3 value = -0.353 804->805 810 mae = 0.0 samples = 1 value = -0.293 804->810 806 month_03 ≤ 0.5 mae = 0.009 samples = 2 value = -0.343 805->806 809 mae = 0.0 samples = 1 value = -0.666 805->809 807 mae = 0.0 samples = 1 value = -0.353 806->807 808 mae = 0.0 samples = 1 value = -0.334 806->808 812 mae = 0.0 samples = 1 value = -0.421 811->812 813 dcoilwtico ≤ 0.557 mae = 0.001 samples = 2 value = -0.21 811->813 814 mae = 0.0 samples = 1 value = -0.208 813->814 815 mae = 0.0 samples = 1 value = -0.211 813->815 817 dcoilwtico ≤ 0.686 mae = 0.131 samples = 8 value = -0.613 816->817 830 mae = 0.0 samples = 1 value = -0.174 816->830 818 month_02 ≤ 0.5 mae = 0.1 samples = 5 value = -0.7 817->818 825 month_02 ≤ 0.5 mae = 0.071 samples = 3 value = -0.473 817->825 819 month_01 ≤ 0.5 mae = 0.06 samples = 4 value = -0.656 818->819 824 mae = 0.0 samples = 1 value = -0.962 818->824 820 mae = 0.0 samples = 2 value = -0.613 819->820 821 dcoilwtico ≤ 0.639 mae = 0.033 samples = 2 value = -0.733 819->821 822 mae = 0.0 samples = 1 value = -0.767 821->822 823 mae = 0.0 samples = 1 value = -0.7 821->823 826 month_03 ≤ 0.5 mae = 0.044 samples = 2 value = -0.517 825->826 829 mae = 0.0 samples = 1 value = -0.349 825->829 827 mae = 0.0 samples = 1 value = -0.473 826->827 828 mae = 0.0 samples = 1 value = -0.561 826->828 832 dcoilwtico ≤ 0.53 mae = 0.003 samples = 2 value = 0.326 831->832 835 mae = 0.0 samples = 1 value = -0.338 831->835 833 mae = 0.0 samples = 1 value = 0.323 832->833 834 mae = 0.0 samples = 1 value = 0.328 832->834 837 month_08 ≤ 0.5 mae = 0.195 samples = 16 value = -0.19 836->837 868 mae = 0.0 samples = 1 value = 0.418 836->868 838 month_09 ≤ 0.5 mae = 0.174 samples = 14 value = -0.209 837->838 865 dcoilwtico ≤ 0.737 mae = 0.077 samples = 2 value = 0.147 837->865 839 month_02 ≤ 0.5 mae = 0.152 samples = 13 value = -0.198 838->839 864 mae = 0.0 samples = 1 value = -0.665 838->864 840 locale_None ≤ 0.5 mae = 0.116 samples = 10 value = -0.19 839->840 859 dcoilwtico ≤ 0.89 mae = 0.233 samples = 3 value = -0.31 839->859 841 mae = 0.0 samples = 1 value = -0.286 840->841 842 dcoilwtico ≤ 0.873 mae = 0.117 samples = 9 value = -0.182 840->842 843 month_04 ≤ 0.5 mae = 0.047 samples = 3 value = -0.198 842->843 848 month_07 ≤ 0.5 mae = 0.131 samples = 6 value = -0.113 842->848 844 dcoilwtico ≤ 0.735 mae = 0.008 samples = 2 value = -0.19 843->844 847 mae = 0.0 samples = 1 value = -0.322 843->847 845 mae = 0.0 samples = 1 value = -0.182 844->845 846 mae = 0.0 samples = 1 value = -0.198 844->846 849 dcoilwtico ≤ 0.977 mae = 0.067 samples = 3 value = -0.004 848->849 854 dcoilwtico ≤ 0.926 mae = 0.123 samples = 3 value = -0.22 848->854 850 dcoilwtico ≤ 0.924 mae = 0.039 samples = 2 value = 0.035 849->850 853 mae = 0.0 samples = 1 value = -0.126 849->853 851 mae = 0.0 samples = 1 value = 0.075 850->851 852 mae = 0.0 samples = 1 value = -0.004 850->852 855 mae = 0.0 samples = 1 value = -0.1 854->855 856 dcoilwtico ≤ 0.975 mae = 0.125 samples = 2 value = -0.345 854->856 857 mae = 0.0 samples = 1 value = -0.47 856->857 858 mae = 0.0 samples = 1 value = -0.22 856->858 860 dcoilwtico ≤ 0.837 mae = 0.18 samples = 2 value = -0.13 859->860 863 mae = 0.0 samples = 1 value = -0.65 859->863 861 mae = 0.0 samples = 1 value = -0.31 860->861 862 mae = 0.0 samples = 1 value = 0.049 860->862 866 mae = 0.0 samples = 1 value = 0.069 865->866 867 mae = 0.0 samples = 1 value = 0.224 865->867 870 dcoilwtico ≤ 1.131 mae = 0.188 samples = 9 value = -0.426 869->870 887 mae = 0.0 samples = 1 value = -0.736 869->887 871 month_06 ≤ 0.5 mae = 0.085 samples = 6 value = -0.516 870->871 882 dcoilwtico ≤ 1.144 mae = 0.227 samples = 3 value = -0.179 870->882 872 dcoilwtico ≤ 1.11 mae = 0.062 samples = 5 value = -0.568 871->872 881 mae = 0.0 samples = 1 value = -0.371 871->881 873 month_07 ≤ 0.5 mae = 0.042 samples = 4 value = -0.571 872->873 880 mae = 0.0 samples = 1 value = -0.426 872->880 874 dcoilwtico ≤ 1.091 mae = 0.02 samples = 3 value = -0.574 873->874 879 mae = 0.0 samples = 1 value = -0.463 873->879 875 month_09 ≤ 0.5 mae = 0.026 samples = 2 value = -0.6 874->875 878 mae = 0.0 samples = 1 value = -0.568 874->878 876 mae = 0.0 samples = 1 value = -0.626 875->876 877 mae = 0.0 samples = 1 value = -0.574 875->877 883 mae = 0.0 samples = 1 value = 0.346 882->883 884 dcoilwtico ≤ 1.155 mae = 0.078 samples = 2 value = -0.257 882->884 885 mae = 0.0 samples = 1 value = -0.335 884->885 886 mae = 0.0 samples = 1 value = -0.179 884->886 889 mae = 0.0 samples = 1 value = -0.749 888->889 890 mae = 0.0 samples = 1 value = -0.708 888->890 892 month_07 ≤ 0.5 mae = 0.013 samples = 2 value = -0.633 891->892 895 mae = 0.0 samples = 1 value = -0.767 891->895 893 mae = 0.0 samples = 1 value = -0.646 892->893 894 mae = 0.0 samples = 1 value = -0.62 892->894 897 dcoilwtico ≤ 0.746 mae = 0.058 samples = 2 value = 0.046 896->897 900 mae = 0.0 samples = 1 value = -0.059 896->900 898 mae = 0.0 samples = 1 value = -0.012 897->898 899 mae = 0.0 samples = 1 value = 0.104 897->899 903 dcoilwtico ≤ -0.752 mae = 0.592 samples = 33 value = 0.683 902->903 968 dcoilwtico ≤ -0.71 mae = 1.641 samples = 13 value = 3.478 902->968 904 day_Wednesday ≤ 0.5 mae = 0.637 samples = 17 value = 0.818 903->904 937 dcoilwtico ≤ 0.756 mae = 0.408 samples = 16 value = 0.35 903->937 905 dcoilwtico ≤ -1.087 mae = 0.58 samples = 14 value = 0.813 904->905 932 dcoilwtico ≤ -1.321 mae = 0.454 samples = 3 value = 2.153 904->932 906 dcoilwtico ≤ -1.861 mae = 0.503 samples = 9 value = 0.427 905->906 923 dcoilwtico ≤ -0.94 mae = 0.479 samples = 5 value = 1.359 905->923 907 mae = 0.0 samples = 1 value = 2.004 906->907 908 dcoilwtico ≤ -1.769 mae = 0.369 samples = 8 value = 0.32 906->908 909 day_Monday ≤ 0.5 mae = 0.308 samples = 4 value = 0.62 908->909 916 dcoilwtico ≤ -1.369 mae = 0.217 samples = 4 value = -0.009 908->916 910 day_Friday ≤ 0.5 mae = 0.123 samples = 2 value = 0.936 909->910 913 dcoilwtico ≤ -1.813 mae = 0.107 samples = 2 value = 0.32 909->913 911 mae = 0.0 samples = 1 value = 0.813 910->911 912 mae = 0.0 samples = 1 value = 1.059 910->912 914 mae = 0.0 samples = 1 value = 0.427 913->914 915 mae = 0.0 samples = 1 value = 0.213 913->915 917 day_Friday ≤ 0.5 mae = 0.041 samples = 3 value = -0.019 916->917 922 mae = 0.0 samples = 1 value = 0.725 916->922 918 locale_Local ≤ 0.5 mae = 0.01 samples = 2 value = -0.009 917->918 921 mae = 0.0 samples = 1 value = -0.122 917->921 919 mae = 0.0 samples = 1 value = 0.001 918->919 920 mae = 0.0 samples = 1 value = -0.019 918->920 924 dcoilwtico ≤ -0.978 mae = 0.271 samples = 3 value = 1.896 923->924 929 dcoilwtico ≤ -0.875 mae = 0.023 samples = 2 value = 0.836 923->929 925 dcoilwtico ≤ -1.035 mae = 0.268 samples = 2 value = 1.627 924->925 928 mae = 0.0 samples = 1 value = 2.171 924->928 926 mae = 0.0 samples = 1 value = 1.896 925->926 927 mae = 0.0 samples = 1 value = 1.359 925->927 930 mae = 0.0 samples = 1 value = 0.86 929->930 931 mae = 0.0 samples = 1 value = 0.813 929->931 933 dcoilwtico ≤ -1.733 mae = 0.668 samples = 2 value = 1.486 932->933 936 mae = 0.0 samples = 1 value = 2.18 932->936 934 mae = 0.0 samples = 1 value = 2.153 933->934 935 mae = 0.0 samples = 1 value = 0.818 933->935 938 dcoilwtico ≤ 0.667 mae = 0.291 samples = 14 value = 0.27 937->938 965 day_Friday ≤ 0.5 mae = 0.483 samples = 2 value = 1.5 937->965 939 dcoilwtico ≤ 0.02 mae = 0.153 samples = 7 value = 0.117 938->939 952 type_Additional ≤ 0.5 mae = 0.333 samples = 7 value = 0.467 938->952 940 type_Additional ≤ 0.5 mae = 0.095 samples = 5 value = 0.262 939->940 949 day_Tuesday ≤ 0.5 mae = 0.019 samples = 2 value = -0.108 939->949 941 dcoilwtico ≤ -0.613 mae = 0.076 samples = 4 value = 0.189 940->941 948 mae = 0.0 samples = 1 value = 0.432 940->948 942 mae = 0.0 samples = 1 value = 0.274 941->942 943 day_Wednesday ≤ 0.5 mae = 0.049 samples = 3 value = 0.117 941->943 944 day_Thursday ≤ 0.5 mae = 0.001 samples = 2 value = 0.116 943->944 947 mae = 0.0 samples = 1 value = 0.262 943->947 945 mae = 0.0 samples = 1 value = 0.115 944->945 946 mae = 0.0 samples = 1 value = 0.117 944->946 950 mae = 0.0 samples = 1 value = -0.088 949->950 951 mae = 0.0 samples = 1 value = -0.127 949->951 953 day_Monday ≤ 0.5 mae = 0.238 samples = 6 value = 0.575 952->953 964 mae = 0.0 samples = 1 value = -0.434 952->964 954 dcoilwtico ≤ 0.691 mae = 0.155 samples = 5 value = 0.467 953->954 963 mae = 0.0 samples = 1 value = 1.121 953->963 955 mae = 0.0 samples = 1 value = 0.785 954->955 956 transferred_None ≤ 0.5 mae = 0.114 samples = 4 value = 0.446 954->956 957 mae = 0.0 samples = 1 value = 0.267 956->957 958 dcoilwtico ≤ 0.716 mae = 0.086 samples = 3 value = 0.467 956->958 959 day_Wednesday ≤ 0.5 mae = 0.021 samples = 2 value = 0.446 958->959 962 mae = 0.0 samples = 1 value = 0.683 958->962 960 mae = 0.0 samples = 1 value = 0.467 959->960 961 mae = 0.0 samples = 1 value = 0.426 959->961 966 mae = 0.0 samples = 1 value = 1.983 965->966 967 mae = 0.0 samples = 1 value = 1.017 965->967 969 day_Friday ≤ 0.5 mae = 1.208 samples = 9 value = 4.242 968->969 986 type_Event ≤ 0.5 mae = 1.193 samples = 4 value = 1.014 968->986 970 dcoilwtico ≤ -0.995 mae = 1.057 samples = 8 value = 4.256 969->970 985 mae = 0.0 samples = 1 value = 1.826 969->985 971 dcoilwtico ≤ -1.854 mae = 1.09 samples = 7 value = 4.242 970->971 984 mae = 0.0 samples = 1 value = 5.067 970->984 972 mae = 0.0 samples = 1 value = 3.478 971->972 973 dcoilwtico ≤ -1.809 mae = 1.144 samples = 6 value = 4.256 971->973 974 mae = 0.0 samples = 1 value = 4.242 973->974 975 dcoilwtico ≤ -1.788 mae = 1.368 samples = 5 value = 4.271 973->975 976 mae = 0.0 samples = 1 value = 5.386 975->976 977 dcoilwtico ≤ -1.77 mae = 1.431 samples = 4 value = 2.936 975->977 978 mae = 0.0 samples = 1 value = 1.454 977->978 979 day_Wednesday ≤ 0.5 mae = 0.968 samples = 3 value = 4.271 977->979 980 dcoilwtico ≤ -1.393 mae = 0.117 samples = 2 value = 4.389 979->980 983 mae = 0.0 samples = 1 value = 1.601 979->983 981 mae = 0.0 samples = 1 value = 4.506 980->981 982 mae = 0.0 samples = 1 value = 4.271 980->982 987 day_Monday ≤ 0.5 mae = 1.412 samples = 3 value = 1.015 986->987 992 mae = 0.0 samples = 1 value = 0.476 986->992 988 day_Thursday ≤ 0.5 mae = 0.001 samples = 2 value = 1.014 987->988 991 mae = 0.0 samples = 1 value = 5.248 987->991 989 mae = 0.0 samples = 1 value = 1.015 988->989 990 mae = 0.0 samples = 1 value = 1.013 988->990 994 month_12 ≤ 0.5 mae = 0.435 samples = 60 value = 0.948 993->994 1113 month_03 ≤ 0.5 mae = 0.549 samples = 41 value = 0.443 993->1113 995 month_09 ≤ 0.5 mae = 0.365 samples = 54 value = 0.923 994->995 1102 dcoilwtico ≤ -1.854 mae = 0.628 samples = 6 value = 1.882 994->1102 996 month_07 ≤ 0.5 mae = 0.349 samples = 50 value = 0.912 995->996 1095 dcoilwtico ≤ -0.46 mae = 0.302 samples = 4 value = 1.596 995->1095 997 month_06 ≤ 0.5 mae = 0.324 samples = 48 value = 0.923 996->997 1092 dcoilwtico ≤ -1.121 mae = 0.479 samples = 2 value = -0.02 996->1092 998 locale_Local ≤ 0.5 mae = 0.31 samples = 43 value = 0.945 997->998 1083 dcoilwtico ≤ -0.839 mae = 0.279 samples = 5 value = 0.396 997->1083 999 dcoilwtico ≤ 0.587 mae = 0.31 samples = 40 value = 0.923 998->999 1078 month_08 ≤ 0.5 mae = 0.081 samples = 3 value = 1.164 998->1078 1000 month_08 ≤ 0.5 mae = 0.278 samples = 37 value = 0.945 999->1000 1073 transferred_None ≤ 0.5 mae = 0.416 samples = 3 value = 0.213 999->1073 1001 transferred_None ≤ 0.5 mae = 0.242 samples = 33 value = 0.983 1000->1001 1066 dcoilwtico ≤ -1.375 mae = 0.413 samples = 4 value = 0.627 1000->1066 1002 month_05 ≤ 0.5 mae = 0.387 samples = 2 value = 0.412 1001->1002 1005 month_05 ≤ 0.5 mae = 0.221 samples = 31 value = 0.988 1001->1005 1003 mae = 0.0 samples = 1 value = 0.025 1002->1003 1004 mae = 0.0 samples = 1 value = 0.8 1002->1004 1006 dcoilwtico ≤ -1.561 mae = 0.213 samples = 29 value = 0.983 1005->1006 1063 dcoilwtico ≤ -0.835 mae = 0.164 samples = 2 value = 1.317 1005->1063 1007 mae = 0.0 samples = 1 value = 1.276 1006->1007 1008 dcoilwtico ≤ -1.506 mae = 0.21 samples = 28 value = 0.967 1006->1008 1009 mae = 0.0 samples = 1 value = 0.581 1008->1009 1010 dcoilwtico ≤ -1.337 mae = 0.203 samples = 27 value = 0.983 1008->1010 1011 month_03 ≤ 0.5 mae = 0.125 samples = 5 value = 1.031 1010->1011 1020 dcoilwtico ≤ -1.315 mae = 0.209 samples = 22 value = 0.938 1010->1020 1012 dcoilwtico ≤ -1.403 mae = 0.121 samples = 4 value = 1.018 1011->1012 1019 mae = 0.0 samples = 1 value = 1.173 1011->1019 1013 dcoilwtico ≤ -1.452 mae = 0.212 samples = 2 value = 1.242 1012->1013 1016 dcoilwtico ≤ -1.37 mae = 0.004 samples = 2 value = 1.001 1012->1016 1014 mae = 0.0 samples = 1 value = 1.031 1013->1014 1015 mae = 0.0 samples = 1 value = 1.454 1013->1015 1017 mae = 0.0 samples = 1 value = 0.997 1016->1017 1018 mae = 0.0 samples = 1 value = 1.005 1016->1018 1021 mae = 0.0 samples = 1 value = 0.391 1020->1021 1022 dcoilwtico ≤ -1.259 mae = 0.192 samples = 21 value = 0.945 1020->1022 1023 dcoilwtico ≤ -1.298 mae = 0.231 samples = 3 value = 1.306 1022->1023 1028 month_10 ≤ 0.5 mae = 0.166 samples = 18 value = 0.938 1022->1028 1024 mae = 0.0 samples = 1 value = 0.814 1023->1024 1025 dcoilwtico ≤ -1.278 mae = 0.101 samples = 2 value = 1.406 1023->1025 1026 mae = 0.0 samples = 1 value = 1.306 1025->1026 1027 mae = 0.0 samples = 1 value = 1.507 1025->1027 1029 month_02 ≤ 0.5 mae = 0.127 samples = 15 value = 0.909 1028->1029 1058 dcoilwtico ≤ 0.331 mae = 0.26 samples = 3 value = 1.183 1028->1058 1030 dcoilwtico ≤ 0.459 mae = 0.126 samples = 13 value = 0.932 1029->1030 1055 dcoilwtico ≤ -1.199 mae = 0.061 samples = 2 value = 0.783 1029->1055 1031 month_11 ≤ 0.5 mae = 0.094 samples = 6 value = 0.957 1030->1031 1042 dcoilwtico ≤ 0.55 mae = 0.143 samples = 7 value = 0.865 1030->1042 1032 dcoilwtico ≤ -1.09 mae = 0.109 samples = 4 value = 0.985 1031->1032 1039 dcoilwtico ≤ -0.132 mae = 0.011 samples = 2 value = 0.92 1031->1039 1033 dcoilwtico ≤ -1.198 mae = 0.105 samples = 2 value = 0.878 1032->1033 1036 dcoilwtico ≤ -0.28 mae = 0.109 samples = 2 value = 1.097 1032->1036 1034 mae = 0.0 samples = 1 value = 0.983 1033->1034 1035 mae = 0.0 samples = 1 value = 0.773 1033->1035 1037 mae = 0.0 samples = 1 value = 1.206 1036->1037 1038 mae = 0.0 samples = 1 value = 0.988 1036->1038 1040 mae = 0.0 samples = 1 value = 0.932 1039->1040 1041 mae = 0.0 samples = 1 value = 0.909 1039->1041 1043 month_01 ≤ 0.5 mae = 0.075 samples = 3 value = 0.703 1042->1043 1048 dcoilwtico ≤ 0.56 mae = 0.079 samples = 4 value = 1.008 1042->1048 1044 mae = 0.0 samples = 1 value = 0.575 1043->1044 1045 dcoilwtico ≤ 0.519 mae = 0.049 samples = 2 value = 0.752 1043->1045 1046 mae = 0.0 samples = 1 value = 0.703 1045->1046 1047 mae = 0.0 samples = 1 value = 0.801 1045->1047 1049 dcoilwtico ≤ 0.553 mae = 0.001 samples = 2 value = 1.067 1048->1049 1052 month_11 ≤ 0.5 mae = 0.043 samples = 2 value = 0.908 1048->1052 1050 mae = 0.0 samples = 1 value = 1.068 1049->1050 1051 mae = 0.0 samples = 1 value = 1.065 1049->1051 1053 mae = 0.0 samples = 1 value = 0.95 1052->1053 1054 mae = 0.0 samples = 1 value = 0.865 1052->1054 1056 mae = 0.0 samples = 1 value = 0.844 1055->1056 1057 mae = 0.0 samples = 1 value = 0.722 1055->1057 1059 dcoilwtico ≤ 0.178 mae = 0.119 samples = 2 value = 1.064 1058->1059 1062 mae = 0.0 samples = 1 value = 1.725 1058->1062 1060 mae = 0.0 samples = 1 value = 1.183 1059->1060 1061 mae = 0.0 samples = 1 value = 0.945 1059->1061 1064 mae = 0.0 samples = 1 value = 1.153 1063->1064 1065 mae = 0.0 samples = 1 value = 1.481 1063->1065 1067 dcoilwtico ≤ -1.522 mae = 0.439 samples = 3 value = 0.579 1066->1067 1072 mae = 0.0 samples = 1 value = 0.914 1066->1072 1068 mae = 0.0 samples = 1 value = 0.674 1067->1068 1069 dcoilwtico ≤ -1.444 mae = 0.612 samples = 2 value = -0.032 1067->1069 1070 mae = 0.0 samples = 1 value = -0.644 1069->1070 1071 mae = 0.0 samples = 1 value = 0.579 1069->1071 1074 mae = 0.0 samples = 1 value = 0.865 1073->1074 1075 month_05 ≤ 0.5 mae = 0.298 samples = 2 value = -0.084 1073->1075 1076 mae = 0.0 samples = 1 value = 0.213 1075->1076 1077 mae = 0.0 samples = 1 value = -0.382 1075->1077 1079 month_11 ≤ 0.5 mae = 0.003 samples = 2 value = 1.161 1078->1079 1082 mae = 0.0 samples = 1 value = 1.402 1078->1082 1080 mae = 0.0 samples = 1 value = 1.157 1079->1080 1081 mae = 0.0 samples = 1 value = 1.164 1079->1081 1084 dcoilwtico ≤ -0.864 mae = 0.18 samples = 2 value = 0.965 1083->1084 1087 dcoilwtico ≤ -0.149 mae = 0.062 samples = 3 value = 0.322 1083->1087 1085 mae = 0.0 samples = 1 value = 1.144 1084->1085 1086 mae = 0.0 samples = 1 value = 0.785 1084->1086 1088 mae = 0.0 samples = 1 value = 0.211 1087->1088 1089 type_No_Holiday ≤ 0.5 mae = 0.037 samples = 2 value = 0.359 1087->1089 1090 mae = 0.0 samples = 1 value = 0.396 1089->1090 1091 mae = 0.0 samples = 1 value = 0.322 1089->1091 1093 mae = 0.0 samples = 1 value = 0.459 1092->1093 1094 mae = 0.0 samples = 1 value = -0.498 1092->1094 1096 mae = 0.0 samples = 1 value = 0.836 1095->1096 1097 dcoilwtico ≤ 0.508 mae = 0.1 samples = 3 value = 1.744 1095->1097 1098 mae = 0.0 samples = 1 value = 1.449 1097->1098 1099 transferred_False ≤ 0.5 mae = 0.002 samples = 2 value = 1.746 1097->1099 1100 mae = 0.0 samples = 1 value = 1.748 1099->1100 1101 mae = 0.0 samples = 1 value = 1.744 1099->1101 1103 mae = 0.0 samples = 1 value = 3.423 1102->1103 1104 dcoilwtico ≤ -1.349 mae = 0.429 samples = 5 value = 1.798 1102->1104 1105 dcoilwtico ≤ -1.811 mae = 0.452 samples = 3 value = 1.379 1104->1105 1110 dcoilwtico ≤ -0.21 mae = 0.017 samples = 2 value = 1.983 1104->1110 1106 mae = 0.0 samples = 1 value = 1.798 1105->1106 1107 locale_Local ≤ 0.5 mae = 0.469 samples = 2 value = 0.91 1105->1107 1108 mae = 0.0 samples = 1 value = 0.442 1107->1108 1109 mae = 0.0 samples = 1 value = 1.379 1107->1109 1111 mae = 0.0 samples = 1 value = 1.965 1110->1111 1112 mae = 0.0 samples = 1 value = 2.0 1110->1112 1114 type_Event ≤ 0.5 mae = 0.512 samples = 37 value = 0.431 1113->1114 1187 locale_name_None ≤ 0.5 mae = 0.525 samples = 4 value = 1.169 1113->1187 1115 dcoilwtico ≤ 1.156 mae = 0.51 samples = 34 value = 0.461 1114->1115 1182 dcoilwtico ≤ 0.744 mae = 0.117 samples = 3 value = -0.195 1114->1182 1116 month_04 ≤ 0.5 mae = 0.495 samples = 32 value = 0.437 1115->1116 1179 dcoilwtico ≤ 1.211 mae = 0.236 samples = 2 value = 1.187 1115->1179 1117 month_12 ≤ 0.5 mae = 0.485 samples = 30 value = 0.392 1116->1117 1176 dcoilwtico ≤ 0.943 mae = 0.216 samples = 2 value = 1.076 1116->1176 1118 month_10 ≤ 0.5 mae = 0.414 samples = 27 value = 0.354 1117->1118 1171 dcoilwtico ≤ 0.709 mae = 0.866 samples = 3 value = 1.129 1117->1171 1119 month_02 ≤ 0.5 mae = 0.394 samples = 24 value = 0.353 1118->1119 1166 dcoilwtico ≤ 0.877 mae = 0.418 samples = 3 value = 0.84 1118->1166 1120 dcoilwtico ≤ 1.018 mae = 0.317 samples = 19 value = 0.431 1119->1120 1157 dcoilwtico ≤ 0.762 mae = 0.603 samples = 5 value = 0.023 1119->1157 1121 month_08 ≤ 0.5 mae = 0.299 samples = 11 value = 0.479 1120->1121 1142 dcoilwtico ≤ 1.107 mae = 0.271 samples = 8 value = 0.237 1120->1142 1122 dcoilwtico ≤ 1.004 mae = 0.232 samples = 8 value = 0.437 1121->1122 1137 dcoilwtico ≤ 0.72 mae = 0.294 samples = 3 value = 0.809 1121->1137 1123 month_05 ≤ 0.5 mae = 0.181 samples = 7 value = 0.431 1122->1123 1136 mae = 0.0 samples = 1 value = 1.024 1122->1136 1124 month_09 ≤ 0.5 mae = 0.064 samples = 5 value = 0.352 1123->1124 1133 dcoilwtico ≤ 0.807 mae = 0.386 samples = 2 value = 0.865 1123->1133 1125 dcoilwtico ≤ 0.661 mae = 0.057 samples = 4 value = 0.32 1124->1125 1132 mae = 0.0 samples = 1 value = 0.443 1124->1132 1126 mae = 0.0 samples = 1 value = 0.431 1125->1126 1127 month_07 ≤ 0.5 mae = 0.028 samples = 3 value = 0.288 1125->1127 1128 month_06 ≤ 0.5 mae = 0.011 samples = 2 value = 0.278 1127->1128 1131 mae = 0.0 samples = 1 value = 0.352 1127->1131 1129 mae = 0.0 samples = 1 value = 0.267 1128->1129 1130 mae = 0.0 samples = 1 value = 0.288 1128->1130 1134 mae = 0.0 samples = 1 value = 0.479 1133->1134 1135 mae = 0.0 samples = 1 value = 1.251 1133->1135 1138 mae = 0.0 samples = 1 value = 1.432 1137->1138 1139 dcoilwtico ≤ 0.749 mae = 0.129 samples = 2 value = 0.68 1137->1139 1140 mae = 0.0 samples = 1 value = 0.551 1139->1140 1141 mae = 0.0 samples = 1 value = 0.809 1139->1141 1143 dcoilwtico ≤ 1.085 mae = 0.175 samples = 5 value = 0.077 1142->1143 1152 month_08 ≤ 0.5 mae = 0.142 samples = 3 value = 0.665 1142->1152 1144 dcoilwtico ≤ 1.043 mae = 0.128 samples = 4 value = 0.142 1143->1144 1151 mae = 0.0 samples = 1 value = -0.29 1143->1151 1145 mae = 0.0 samples = 1 value = -0.112 1144->1145 1146 dcoilwtico ≤ 1.084 mae = 0.064 samples = 3 value = 0.207 1144->1146 1147 dcoilwtico ≤ 1.072 mae = 0.031 samples = 2 value = 0.237 1146->1147 1150 mae = 0.0 samples = 1 value = 0.077 1146->1150 1148 mae = 0.0 samples = 1 value = 0.268 1147->1148 1149 mae = 0.0 samples = 1 value = 0.207 1147->1149 1153 dcoilwtico ≤ 1.129 mae = 0.054 samples = 2 value = 0.719 1152->1153 1156 mae = 0.0 samples = 1 value = 0.347 1152->1156 1154 mae = 0.0 samples = 1 value = 0.773 1153->1154 1155 mae = 0.0 samples = 1 value = 0.665 1153->1155 1158 dcoilwtico ≤ 0.684 mae = 0.535 samples = 2 value = -1.054 1157->1158 1161 dcoilwtico ≤ 0.922 mae = 0.177 samples = 3 value = 0.354 1157->1161 1159 mae = 0.0 samples = 1 value = -0.52 1158->1159 1160 mae = 0.0 samples = 1 value = -1.589 1158->1160 1162 dcoilwtico ≤ 0.862 mae = 0.099 samples = 2 value = 0.453 1161->1162 1165 mae = 0.0 samples = 1 value = 0.023 1161->1165 1163 mae = 0.0 samples = 1 value = 0.354 1162->1163 1164 mae = 0.0 samples = 1 value = 0.553 1162->1164 1167 mae = 0.0 samples = 1 value = 1.28 1166->1167 1168 dcoilwtico ≤ 0.949 mae = 0.407 samples = 2 value = 0.433 1166->1168 1169 mae = 0.0 samples = 1 value = 0.027 1168->1169 1170 mae = 0.0 samples = 1 value = 0.84 1168->1170 1172 mae = 0.0 samples = 1 value = 2.449 1171->1172 1173 transferred_None ≤ 0.5 mae = 0.639 samples = 2 value = 0.491 1171->1173 1174 mae = 0.0 samples = 1 value = 1.129 1173->1174 1175 mae = 0.0 samples = 1 value = -0.148 1173->1175 1177 mae = 0.0 samples = 1 value = 1.292 1176->1177 1178 mae = 0.0 samples = 1 value = 0.86 1176->1178 1180 mae = 0.0 samples = 1 value = 1.423 1179->1180 1181 mae = 0.0 samples = 1 value = 0.952 1179->1181 1183 mae = 0.0 samples = 1 value = -0.222 1182->1183 1184 month_05 ≤ 0.5 mae = 0.162 samples = 2 value = -0.034 1182->1184 1185 mae = 0.0 samples = 1 value = -0.195 1184->1185 1186 mae = 0.0 samples = 1 value = 0.128 1184->1186 1188 mae = 0.0 samples = 1 value = -0.458 1187->1188 1189 dcoilwtico ≤ 0.805 mae = 0.157 samples = 3 value = 1.169 1187->1189 1190 mae = 0.0 samples = 1 value = 1.639 1189->1190 1191 dcoilwtico ≤ 0.866 mae = 0.001 samples = 2 value = 1.169 1189->1191 1192 mae = 0.0 samples = 1 value = 1.168 1191->1192 1193 mae = 0.0 samples = 1 value = 1.169 1191->1193 1195 type_Additional ≤ 0.5 mae = 0.418 samples = 102 value = 0.958 1194->1195 1398 locale_National ≤ 0.5 mae = 0.871 samples = 7 value = 2.194 1194->1398 1196 dcoilwtico ≤ 0.94 mae = 0.385 samples = 100 value = 0.941 1195->1196 1395 dcoilwtico ≤ -0.107 mae = 0.341 samples = 2 value = 3.015 1195->1395 1197 dcoilwtico ≤ 0.294 mae = 0.358 samples = 80 value = 1.003 1196->1197 1356 month_05 ≤ 0.5 mae = 0.373 samples = 20 value = 0.579 1196->1356 1198 locale_name_Guayaquil ≤ 0.5 mae = 0.315 samples = 37 value = 1.135 1197->1198 1271 month_08 ≤ 0.5 mae = 0.371 samples = 43 value = 0.909 1197->1271 1199 month_02 ≤ 0.5 mae = 0.306 samples = 36 value = 1.136 1198->1199 1270 mae = 0.0 samples = 1 value = 0.502 1198->1270 1200 month_03 ≤ 0.5 mae = 0.293 samples = 33 value = 1.149 1199->1200 1265 dcoilwtico ≤ -1.142 mae = 0.278 samples = 3 value = 0.801 1199->1265 1201 locale_name_Riobamba ≤ 0.5 mae = 0.298 samples = 30 value = 1.136 1200->1201 1260 dcoilwtico ≤ -1.324 mae = 0.095 samples = 3 value = 1.36 1200->1260 1202 dcoilwtico ≤ -1.429 mae = 0.3 samples = 29 value = 1.135 1201->1202 1259 mae = 0.0 samples = 1 value = 1.395 1201->1259 1203 month_11 ≤ 0.5 mae = 0.234 samples = 7 value = 0.88 1202->1203 1216 dcoilwtico ≤ -1.406 mae = 0.303 samples = 22 value = 1.143 1202->1216 1204 month_09 ≤ 0.5 mae = 0.173 samples = 4 value = 0.81 1203->1204 1211 locale_Regional ≤ 0.5 mae = 0.052 samples = 3 value = 1.21 1203->1211 1205 dcoilwtico ≤ -1.497 mae = 0.197 samples = 3 value = 0.78 1204->1205 1210 mae = 0.0 samples = 1 value = 0.88 1204->1210 1206 month_08 ≤ 0.5 mae = 0.031 samples = 2 value = 0.81 1205->1206 1209 mae = 0.0 samples = 1 value = 0.251 1205->1209 1207 mae = 0.0 samples = 1 value = 0.78 1206->1207 1208 mae = 0.0 samples = 1 value = 0.841 1206->1208 1212 dcoilwtico ≤ -1.663 mae = 0.009 samples = 2 value = 1.219 1211->1212 1215 mae = 0.0 samples = 1 value = 1.073 1211->1215 1213 mae = 0.0 samples = 1 value = 1.21 1212->1213 1214 mae = 0.0 samples = 1 value = 1.228 1212->1214 1217 mae = 0.0 samples = 1 value = 1.557 1216->1217 1218 dcoilwtico ≤ -1.31 mae = 0.298 samples = 21 value = 1.137 1216->1218 1219 dcoilwtico ≤ -1.336 mae = 0.079 samples = 3 value = 1.218 1218->1219 1224 month_01 ≤ 0.5 mae = 0.321 samples = 18 value = 1.131 1218->1224 1220 dcoilwtico ≤ -1.368 mae = 0.001 samples = 2 value = 1.216 1219->1220 1223 mae = 0.0 samples = 1 value = 1.451 1219->1223 1221 mae = 0.0 samples = 1 value = 1.218 1220->1221 1222 mae = 0.0 samples = 1 value = 1.215 1220->1222 1225 dcoilwtico ≤ -1.257 mae = 0.276 samples = 17 value = 1.135 1224->1225 1258 mae = 0.0 samples = 1 value = 0.052 1224->1258 1226 mae = 0.0 samples = 1 value = 0.379 1225->1226 1227 dcoilwtico ≤ -1.196 mae = 0.246 samples = 16 value = 1.136 1225->1227 1228 mae = 0.0 samples = 1 value = 0.761 1227->1228 1229 month_06 ≤ 0.5 mae = 0.237 samples = 15 value = 1.137 1227->1229 1230 dcoilwtico ≤ -1.112 mae = 0.181 samples = 12 value = 1.143 1229->1230 1253 dcoilwtico ≤ -0.839 mae = 0.4 samples = 3 value = 0.948 1229->1253 1231 mae = 0.0 samples = 1 value = 1.012 1230->1231 1232 month_11 ≤ 0.5 mae = 0.185 samples = 11 value = 1.149 1230->1232 1233 month_07 ≤ 0.5 mae = 0.142 samples = 7 value = 1.137 1232->1233 1246 dcoilwtico ≤ -0.036 mae = 0.23 samples = 4 value = 1.25 1232->1246 1234 dcoilwtico ≤ 0.18 mae = 0.151 samples = 6 value = 1.136 1233->1234 1245 mae = 0.0 samples = 1 value = 1.226 1233->1245 1235 dcoilwtico ≤ 0.085 mae = 0.179 samples = 5 value = 1.137 1234->1235 1244 mae = 0.0 samples = 1 value = 1.127 1234->1244 1236 dcoilwtico ≤ -0.856 mae = 0.108 samples = 4 value = 1.136 1235->1236 1243 mae = 0.0 samples = 1 value = 1.599 1235->1243 1237 month_05 ≤ 0.5 mae = 0.208 samples = 2 value = 0.927 1236->1237 1240 month_10 ≤ 0.5 mae = 0.006 samples = 2 value = 1.143 1236->1240 1238 mae = 0.0 samples = 1 value = 1.135 1237->1238 1239 mae = 0.0 samples = 1 value = 0.72 1237->1239 1241 mae = 0.0 samples = 1 value = 1.149 1240->1241 1242 mae = 0.0 samples = 1 value = 1.137 1240->1242 1247 dcoilwtico ≤ -0.348 mae = 0.105 samples = 3 value = 1.298 1246->1247 1252 mae = 0.0 samples = 1 value = 0.691 1246->1252 1248 mae = 0.0 samples = 1 value = 1.517 1247->1248 1249 dcoilwtico ≤ -0.113 mae = 0.048 samples = 2 value = 1.25 1247->1249 1250 mae = 0.0 samples = 1 value = 1.202 1249->1250 1251 mae = 0.0 samples = 1 value = 1.298 1249->1251 1254 dcoilwtico ≤ -0.86 mae = 0.549 samples = 2 value = 1.497 1253->1254 1257 mae = 0.0 samples = 1 value = 0.846 1253->1257 1255 mae = 0.0 samples = 1 value = 0.948 1254->1255 1256 mae = 0.0 samples = 1 value = 2.045 1254->1256 1261 dcoilwtico ≤ -1.432 mae = 0.053 samples = 2 value = 1.307 1260->1261 1264 mae = 0.0 samples = 1 value = 1.539 1260->1264 1262 mae = 0.0 samples = 1 value = 1.36 1261->1262 1263 mae = 0.0 samples = 1 value = 1.254 1261->1263 1266 dcoilwtico ≤ -1.205 mae = 0.13 samples = 2 value = 0.93 1265->1266 1269 mae = 0.0 samples = 1 value = 0.225 1265->1269 1267 mae = 0.0 samples = 1 value = 0.801 1266->1267 1268 mae = 0.0 samples = 1 value = 1.06 1266->1268 1272 month_09 ≤ 0.5 mae = 0.35 samples = 39 value = 0.85 1271->1272 1349 dcoilwtico ≤ 0.728 mae = 0.295 samples = 4 value = 1.469 1271->1349 1273 dcoilwtico ≤ 0.695 mae = 0.328 samples = 38 value = 0.823 1272->1273 1348 mae = 0.0 samples = 1 value = 2.032 1272->1348 1274 dcoilwtico ≤ 0.564 mae = 0.277 samples = 23 value = 0.756 1273->1274 1319 locale_None ≤ 0.5 mae = 0.368 samples = 15 value = 1.043 1273->1319 1275 month_02 ≤ 0.5 mae = 0.237 samples = 11 value = 0.971 1274->1275 1296 type_Work Day ≤ 0.5 mae = 0.263 samples = 12 value = 0.651 1274->1296 1276 dcoilwtico ≤ 0.501 mae = 0.182 samples = 10 value = 0.971 1275->1276 1295 mae = 0.0 samples = 1 value = 0.181 1275->1295 1277 dcoilwtico ≤ 0.389 mae = 0.085 samples = 4 value = 0.726 1276->1277 1284 transferred_None ≤ 0.5 mae = 0.122 samples = 6 value = 1.127 1276->1284 1278 mae = 0.0 samples = 1 value = 0.971 1277->1278 1279 type_No_Holiday ≤ 0.5 mae = 0.021 samples = 3 value = 0.695 1277->1279 1280 mae = 0.0 samples = 1 value = 0.756 1279->1280 1281 month_03 ≤ 0.5 mae = 0.002 samples = 2 value = 0.693 1279->1281 1282 mae = 0.0 samples = 1 value = 0.691 1281->1282 1283 mae = 0.0 samples = 1 value = 0.695 1281->1283 1285 mae = 0.0 samples = 1 value = 0.796 1284->1285 1286 dcoilwtico ≤ 0.509 mae = 0.08 samples = 5 value = 1.128 1284->1286 1287 mae = 0.0 samples = 1 value = 0.972 1286->1287 1288 month_01 ≤ 0.5 mae = 0.061 samples = 4 value = 1.135 1286->1288 1289 month_06 ≤ 0.5 mae = 0.006 samples = 3 value = 1.128 1288->1289 1294 mae = 0.0 samples = 1 value = 1.356 1288->1294 1290 dcoilwtico ≤ 0.55 mae = 0.007 samples = 2 value = 1.135 1289->1290 1293 mae = 0.0 samples = 1 value = 1.125 1289->1293 1291 mae = 0.0 samples = 1 value = 1.143 1290->1291 1292 mae = 0.0 samples = 1 value = 1.128 1290->1292 1297 month_06 ≤ 0.5 mae = 0.24 samples = 11 value = 0.651 1296->1297 1318 mae = 0.0 samples = 1 value = 0.131 1296->1318 1298 month_01 ≤ 0.5 mae = 0.238 samples = 10 value = 0.679 1297->1298 1317 mae = 0.0 samples = 1 value = 0.39 1297->1317 1299 dcoilwtico ≤ 0.61 mae = 0.265 samples = 7 value = 0.85 1298->1299 1312 dcoilwtico ≤ 0.668 mae = 0.07 samples = 3 value = 0.65 1298->1312 1300 month_05 ≤ 0.5 mae = 0.309 samples = 4 value = 0.661 1299->1300 1307 dcoilwtico ≤ 0.652 mae = 0.064 samples = 3 value = 0.993 1299->1307 1301 dcoilwtico ≤ 0.569 mae = 0.083 samples = 3 value = 0.707 1300->1301 1306 mae = 0.0 samples = 1 value = -0.279 1300->1306 1302 mae = 0.0 samples = 1 value = 0.615 1301->1302 1303 month_11 ≤ 0.5 mae = 0.078 samples = 2 value = 0.786 1301->1303 1304 mae = 0.0 samples = 1 value = 0.864 1303->1304 1305 mae = 0.0 samples = 1 value = 0.707 1303->1305 1308 month_05 ≤ 0.5 mae = 0.071 samples = 2 value = 0.922 1307->1308 1311 mae = 0.0 samples = 1 value = 1.043 1307->1311 1309 mae = 0.0 samples = 1 value = 0.993 1308->1309 1310 mae = 0.0 samples = 1 value = 0.85 1308->1310 1313 dcoilwtico ≤ 0.648 mae = 0.104 samples = 2 value = 0.546 1312->1313 1316 mae = 0.0 samples = 1 value = 0.651 1312->1316 1314 mae = 0.0 samples = 1 value = 0.65 1313->1314 1315 mae = 0.0 samples = 1 value = 0.442 1313->1315 1320 mae = 0.0 samples = 1 value = 0.655 1319->1320 1321 month_10 ≤ 0.5 mae = 0.367 samples = 14 value = 1.059 1319->1321 1322 dcoilwtico ≤ 0.868 mae = 0.283 samples = 11 value = 1.075 1321->1322 1343 dcoilwtico ≤ 0.886 mae = 0.537 samples = 3 value = 0.669 1321->1343 1323 month_05 ≤ 0.5 mae = 0.329 samples = 7 value = 0.934 1322->1323 1336 dcoilwtico ≤ 0.87 mae = 0.156 samples = 4 value = 1.128 1322->1336 1324 dcoilwtico ≤ 0.711 mae = 0.296 samples = 6 value = 0.857 1323->1324 1335 mae = 0.0 samples = 1 value = 1.465 1323->1335 1325 mae = 0.0 samples = 1 value = 1.123 1324->1325 1326 dcoilwtico ≤ 0.714 mae = 0.286 samples = 5 value = 0.78 1324->1326 1327 mae = 0.0 samples = 1 value = -0.196 1326->1327 1328 dcoilwtico ≤ 0.81 mae = 0.114 samples = 4 value = 0.857 1326->1328 1329 dcoilwtico ≤ 0.751 mae = 0.07 samples = 2 value = 1.005 1328->1329 1332 dcoilwtico ≤ 0.85 mae = 0.003 samples = 2 value = 0.777 1328->1332 1330 mae = 0.0 samples = 1 value = 1.075 1329->1330 1331 mae = 0.0 samples = 1 value = 0.934 1329->1331 1333 mae = 0.0 samples = 1 value = 0.78 1332->1333 1334 mae = 0.0 samples = 1 value = 0.774 1332->1334 1337 mae = 0.0 samples = 1 value = 1.607 1336->1337 1338 month_03 ≤ 0.5 mae = 0.038 samples = 3 value = 1.099 1336->1338 1339 dcoilwtico ≤ 0.903 mae = 0.028 samples = 2 value = 1.071 1338->1339 1342 mae = 0.0 samples = 1 value = 1.157 1338->1342 1340 mae = 0.0 samples = 1 value = 1.099 1339->1340 1341 mae = 0.0 samples = 1 value = 1.043 1339->1341 1344 dcoilwtico ≤ 0.794 mae = 0.394 samples = 2 value = 1.063 1343->1344 1347 mae = 0.0 samples = 1 value = -0.155 1343->1347 1345 mae = 0.0 samples = 1 value = 0.669 1344->1345 1346 mae = 0.0 samples = 1 value = 1.457 1344->1346 1350 dcoilwtico ≤ 0.699 mae = 0.186 samples = 3 value = 1.531 1349->1350 1355 mae = 0.0 samples = 1 value = 0.909 1349->1355 1351 dcoilwtico ≤ 0.641 mae = 0.216 samples = 2 value = 1.747 1350->1351 1354 mae = 0.0 samples = 1 value = 1.406 1350->1354 1352 mae = 0.0 samples = 1 value = 1.531 1351->1352 1353 mae = 0.0 samples = 1 value = 1.963 1351->1353 1357 month_06 ≤ 0.5 mae = 0.348 samples = 18 value = 0.546 1356->1357 1392 transferred_None ≤ 0.5 mae = 0.1 samples = 2 value = 1.16 1356->1392 1358 dcoilwtico ≤ 1.205 mae = 0.196 samples = 14 value = 0.492 1357->1358 1385 locale_None ≤ 0.5 mae = 0.663 samples = 4 value = 1.136 1357->1385 1359 dcoilwtico ≤ 1.011 mae = 0.168 samples = 13 value = 0.454 1358->1359 1384 mae = 0.0 samples = 1 value = 1.004 1358->1384 1360 dcoilwtico ≤ 0.972 mae = 0.086 samples = 4 value = 0.64 1359->1360 1367 dcoilwtico ≤ 1.148 mae = 0.15 samples = 9 value = 0.368 1359->1367 1361 transferred_None ≤ 0.5 mae = 0.071 samples = 2 value = 0.525 1360->1361 1364 month_07 ≤ 0.5 mae = 0.013 samples = 2 value = 0.697 1360->1364 1362 mae = 0.0 samples = 1 value = 0.595 1361->1362 1363 mae = 0.0 samples = 1 value = 0.454 1361->1363 1365 mae = 0.0 samples = 1 value = 0.71 1364->1365 1366 mae = 0.0 samples = 1 value = 0.685 1364->1366 1368 dcoilwtico ≤ 1.142 mae = 0.129 samples = 8 value = 0.352 1367->1368 1383 mae = 0.0 samples = 1 value = 0.685 1367->1383 1369 dcoilwtico ≤ 1.128 mae = 0.111 samples = 7 value = 0.368 1368->1369 1382 mae = 0.0 samples = 1 value = 0.115 1368->1382 1370 month_07 ≤ 0.5 mae = 0.103 samples = 6 value = 0.352 1369->1370 1381 mae = 0.0 samples = 1 value = 0.53 1369->1381 1371 dcoilwtico ≤ 1.104 mae = 0.018 samples = 3 value = 0.368 1370->1371 1376 dcoilwtico ≤ 1.041 mae = 0.148 samples = 3 value = 0.252 1370->1376 1372 dcoilwtico ≤ 1.052 mae = 0.011 samples = 2 value = 0.379 1371->1372 1375 mae = 0.0 samples = 1 value = 0.335 1371->1375 1373 mae = 0.0 samples = 1 value = 0.368 1372->1373 1374 mae = 0.0 samples = 1 value = 0.39 1372->1374 1377 mae = 0.0 samples = 1 value = 0.117 1376->1377 1378 dcoilwtico ≤ 1.067 mae = 0.155 samples = 2 value = 0.407 1376->1378 1379 mae = 0.0 samples = 1 value = 0.562 1378->1379 1380 mae = 0.0 samples = 1 value = 0.252 1378->1380 1386 mae = 0.0 samples = 1 value = 0.237 1385->1386 1387 dcoilwtico ≤ 1.144 mae = 0.528 samples = 3 value = 1.304 1385->1387 1388 dcoilwtico ≤ 1.068 mae = 0.624 samples = 2 value = 1.928 1387->1388 1391 mae = 0.0 samples = 1 value = 0.968 1387->1391 1389 mae = 0.0 samples = 1 value = 1.304 1388->1389 1390 mae = 0.0 samples = 1 value = 2.551 1388->1390 1393 mae = 0.0 samples = 1 value = 1.06 1392->1393 1394 mae = 0.0 samples = 1 value = 1.26 1392->1394 1396 mae = 0.0 samples = 1 value = 3.357 1395->1396 1397 mae = 0.0 samples = 1 value = 2.674 1395->1397 1399 transferred_None ≤ 0.5 mae = 0.678 samples = 5 value = 2.17 1398->1399 1408 type_Work Day ≤ 0.5 mae = 0.097 samples = 2 value = 3.535 1398->1408 1400 mae = 0.0 samples = 1 value = 0.597 1399->1400 1401 dcoilwtico ≤ 0.705 mae = 0.455 samples = 4 value = 2.182 1399->1401 1402 dcoilwtico ≤ -0.129 mae = 0.146 samples = 3 value = 2.194 1401->1402 1407 mae = 0.0 samples = 1 value = 0.814 1401->1407 1403 dcoilwtico ≤ -1.389 mae = 0.208 samples = 2 value = 2.401 1402->1403 1406 mae = 0.0 samples = 1 value = 2.17 1402->1406 1404 mae = 0.0 samples = 1 value = 2.194 1403->1404 1405 mae = 0.0 samples = 1 value = 2.609 1403->1405 1409 mae = 0.0 samples = 1 value = 3.438 1408->1409 1410 mae = 0.0 samples = 1 value = 3.632 1408->1410

Model 3 : Random Forest with GridsearchCV

In [115]:
# Choose the type of classifier. 
RFR = RandomForestRegressor()

# Choose some parameter combinations to try
parameters = {'n_estimators': [5, 10, 100],
              'criterion': ['mse','mae'],
              'max_depth': [5, 10, 15], 
              'min_samples_split': [2, 5, 10],
              'min_samples_leaf': [1,5]
             }

# Type of scoring used to compare parameter combinations

# Run the grid search
grid_obj = GridSearchCV(RFR, parameters,
                        cv=5, #Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold
                        n_jobs=-1, #Number of jobs to run in parallel
                        verbose=1)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
RFR = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
RFR.fit(X_train, y_train)
Fitting 5 folds for each of 108 candidates, totalling 540 fits
[Parallel(n_jobs=-1)]: Done 212 tasks      | elapsed:    3.8s
[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:   29.7s finished
Out[115]:
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=15,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=10,
           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False)
In [116]:
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error

predictions = RFR.predict(X_test)
R2_RF_WithGCV_Tran_Pred = r2_score(Y_test, predictions)

print('R2 score = ',R2_RF_WithGCV_Tran_Pred, '/ 1.0')
print("The mean absolute error of random forest with gridsearchcv is : " + str(mean_absolute_error(Y_test, predictions)))
print("The mean squared error of random forest with gridsearchcv is : " + str(mean_squared_error(Y_test, predictions)))
print("The root mean squared error of random forest with gridsearchcv is : " + str(np.sqrt(mean_squared_error(Y_test, predictions))))
('R2 score = ', 0.7407277036111519, '/ 1.0')
The mean absolute error of random forest with gridsearchcv is : 0.3517972465249688
The mean squared error of random forest with gridsearchcv is : 0.2495734439004096
The root mean squared error of random forest with gridsearchcv is : 0.4995732617949139
In [117]:
RMSE_RF_WithGCV_Tran_Pred = np.sqrt(mean_squared_error(Y_test, predictions))
In [118]:
#Check and plot the 500 first predictions
plt.plot(Y_test.as_matrix()[0:500], '+', color ='red', alpha=0.7)
plt.plot(predictions[0:500], 'ro', color ='green', alpha=0.5)
plt.show()

Sales Prediction

In [119]:
# Modeling for Total sales as the dependent variable and all the other variables except transactions as the independent variables.
In [120]:
X_train = pd_train.drop(['unit_sales','transactions'], axis = 1)
y_labels = pd_train['unit_sales']
In [121]:
num_test = 0.20
X_train, X_test, y_train, Y_test = train_test_split(X_train, y_labels, test_size=num_test, random_state=15)
print('X_train shape :', X_train.shape)
print('y_train shape :', y_train.shape)
print('X_test shape :', X_test.shape)
print('Y_test shape :', Y_test.shape)
('X_train shape :', (871, 55))
('y_train shape :', (871,))
('X_test shape :', (218, 55))
('Y_test shape :', (218,))

Model1: Linear regression

invoke the LinearRegression function and find the bestfit model on training data

In [122]:
regression_model = LinearRegression()
regression_model.fit(X_train.as_matrix(), y_train)
Out[122]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
In [123]:
print(regression_model.coef_)
print(regression_model.intercept_)
[-3.56671554e-01 -1.13197940e+10 -1.13197940e+10 -1.13197940e+10
 -1.13197940e+10 -5.93692747e+11 -1.13197940e+10 -1.13197940e+10
  8.67527305e+09 -4.14227069e+11  6.04197336e+11 -2.20108576e+12
 -2.44828712e+11  4.92031736e+10  4.92031736e+10 -1.20195183e+11
  4.92031736e+10 -9.69221231e+11  4.92031736e+10  4.92031736e+10
  4.92031736e+10  4.92031736e+10  4.92031736e+10  4.92031736e+10
  4.92031736e+10  4.92031736e+10  4.60354116e+11  4.92031736e+10
  4.92031736e+10  4.92031736e+10  4.92031736e+10 -1.20195183e+11
 -1.20195183e+11 -1.49717495e+12 -1.49717495e+12  4.60905745e+11
  1.75291489e+11  1.75291489e+11  1.75291489e+11  1.75291489e+11
  1.75291489e+11  1.75291489e+11  1.75291489e+11  1.75291489e+11
  1.75291489e+11  1.75291489e+11  1.75291489e+11  1.75291489e+11
 -1.12300798e+11 -1.12300798e+11 -1.12300798e+11 -1.12300798e+11
 -1.12300798e+11 -1.12300798e+11 -1.12300798e+11]
1801852679207.4539
In [124]:
# Checking the coefficients for each of the independent attributes

for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, regression_model.coef_[idx]))
The coefficient for dcoilwtico is -0.35667155353
The coefficient for type_Additional is -11319793989.6
The coefficient for type_Bridge is -11319793989.8
The coefficient for type_Event is -11319793989.7
The coefficient for type_Holiday is -11319793989.3
The coefficient for type_No_Holiday is -5.93692747025e+11
The coefficient for type_Transfer is -11319793991.2
The coefficient for type_Work Day is -11319793990.3
The coefficient for store_nbr_44 is 8675273049.01
The coefficient for locale_Local is -4.14227068769e+11
The coefficient for locale_National is 6.04197335911e+11
The coefficient for locale_None is -2.20108575761e+12
The coefficient for locale_Regional is -2.44828712195e+11
The coefficient for locale_name_Ambato is 49203173571.9
The coefficient for locale_name_Cayambe is 49203173571.8
The coefficient for locale_name_Cotopaxi is -1.20195183002e+11
The coefficient for locale_name_Cuenca is 49203173571.5
The coefficient for locale_name_Ecuador is -9.69221231108e+11
The coefficient for locale_name_Esmeraldas is 49203173571.8
The coefficient for locale_name_Guaranda is 49203173571.7
The coefficient for locale_name_Guayaquil is 49203173572.0
The coefficient for locale_name_Ibarra is 49203173571.8
The coefficient for locale_name_Latacunga is 49203173571.7
The coefficient for locale_name_Libertad is 49203173571.7
The coefficient for locale_name_Loja is 49203173571.0
The coefficient for locale_name_Manta is 49203173571.6
The coefficient for locale_name_None is 4.60354116119e+11
The coefficient for locale_name_Puyo is 49203173571.7
The coefficient for locale_name_Quevedo is 49203173571.6
The coefficient for locale_name_Quito is 49203173571.8
The coefficient for locale_name_Riobamba is 49203173572.0
The coefficient for locale_name_Santa Elena is -1.20195183002e+11
The coefficient for locale_name_Santo Domingo de los Tsachilas is -1.20195183003e+11
The coefficient for transferred_False is -1.49717495408e+12
The coefficient for transferred_True is -1.49717495409e+12
The coefficient for transferred_None is 4.60905745243e+11
The coefficient for month_01 is 1.75291488712e+11
The coefficient for month_02 is 1.75291488712e+11
The coefficient for month_03 is 1.75291488712e+11
The coefficient for month_04 is 1.75291488712e+11
The coefficient for month_05 is 1.75291488712e+11
The coefficient for month_06 is 1.75291488712e+11
The coefficient for month_07 is 1.75291488712e+11
The coefficient for month_08 is 1.75291488712e+11
The coefficient for month_09 is 1.75291488713e+11
The coefficient for month_10 is 1.75291488713e+11
The coefficient for month_11 is 1.75291488712e+11
The coefficient for month_12 is 1.75291488713e+11
The coefficient for day_Friday is -1.12300797698e+11
The coefficient for day_Monday is -1.12300797698e+11
The coefficient for day_Saturday is -1.12300797697e+11
The coefficient for day_Sunday is -1.12300797697e+11
The coefficient for day_Thursday is -1.12300797698e+11
The coefficient for day_Tuesday is -1.12300797698e+11
The coefficient for day_Wednesday is -1.12300797698e+11
In [125]:
# Let us check the intercept for the model

intercept = regression_model.intercept_

print("The intercept for the model is {}".format(intercept))
The intercept for the model is 1.80185267921e+12
In [126]:
regression_model.score(X_test, Y_test)
Out[126]:
0.48022163239096594
In [127]:
# checking the sum of squared errors by predicting value of y for test cases and 
# subtracting from the actual y for the test cases

mse = np.mean((regression_model.predict(X_test)-Y_test)**2)
In [128]:
# underroot of mean_sq_error is standard deviation i.e. avg variance between predicted and actual

import math

math.sqrt(mse)
Out[128]:
0.7217543086609938
In [129]:
# predict transaction volume for a set of attributes 
y_pred = regression_model.predict(X_test)
In [130]:
# Since this is regression, plot the predicted y value vs actual y values for the test data
plt.scatter(Y_test, y_pred)
Out[130]:
<matplotlib.collections.PathCollection at 0x7f74d809dfd0>
In [131]:
from sklearn.metrics import r2_score
R2_Lin_Reg_Sale_Pred = r2_score(Y_test, y_pred)
print(R2_Lin_Reg_Sale_Pred)
0.48022163239096594
In [132]:
y_train_pred = regression_model.predict(X_train)
RMSE_Lin_Reg_Sale_Pred = np.sqrt(mean_squared_error(y_train, y_train_pred))

# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Train data
print(mean_absolute_error(y_train, y_train_pred))
print(mean_squared_error(y_train, y_train_pred))
print(RMSE_Lin_Reg_Sale_Pred)
0.5094281855570595
0.41483286813709686
0.6440752037899743
In [133]:
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of simple linear regression model is : " + str(mean_absolute_error(Y_test, y_pred)))
print("The mean squared error of simple linear regression model is : " + str(mean_squared_error(Y_test, y_pred)))
print("The root mean squared error of simple linear regression model is : " + str(np.sqrt(mean_squared_error(Y_test, y_pred))))
The mean absolute error of simple linear regression model is : 0.5402662087696632
The mean squared error of simple linear regression model is : 0.5209292820707088
The root mean squared error of simple linear regression model is : 0.7217543086609935

Model 1b: Linear regression using stochastic gradient descent without regularization

In [134]:
sgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1)
sgd_reg.fit(X_train, y_train.ravel())
/usr/local/anaconda/python2/lib/python2.7/site-packages/sklearn/linear_model/stochastic_gradient.py:117: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.
  DeprecationWarning)
Out[134]:
SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.1,
       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
       loss='squared_loss', max_iter=None, n_iter=50, penalty=None,
       power_t=0.25, random_state=None, shuffle=True, tol=None, verbose=0,
       warm_start=False)
In [135]:
print(sgd_reg.intercept_)
print(sgd_reg.coef_)
[-0.12142162]
[-0.31208552  0.21902805 -0.00572011  0.08854119  0.48504647  0.104221
 -0.55737674 -0.45516148 -0.12142162 -0.28096585  0.09888348  0.104221
 -0.04356025  0.1439799   0.0390537  -0.09307756 -0.13133879  0.09888348
  0.04680912 -0.07273212  0.19455258  0.05193425 -0.06002994 -0.06464013
 -0.45867428 -0.11299947  0.104221   -0.06448825 -0.05184745  0.01543181
  0.24402321  0.27344636 -0.22392904  0.14649808 -0.37214069  0.104221
 -0.07057067 -0.60763891 -0.18327678 -0.51359609 -0.41967586 -0.04175532
  0.21977505 -0.08950534  0.39438081  0.341917    0.20976123  0.63876327
 -0.2859369  -0.2511476   0.69921901  0.86660804 -0.63270941 -0.44444341
 -0.07301136]
In [136]:
sgd_reg.score(X_test, Y_test)
Out[136]:
0.5011674821308041
In [137]:
# checking the sum of squared errors by predicting value of y for test cases and 
# subtracting from the actual y for the test cases
mse = np.mean((sgd_reg.predict(X_test)-Y_test)**2)
In [138]:
# underroot of mean_sq_error is standard deviation i.e. avg variance between predicted and actual
import math
math.sqrt(mse)
Out[138]:
0.7070622696595126
In [139]:
# predict transaction volume for a set of attributes 
y_pred = sgd_reg.predict(X_test)
In [140]:
# Since this is regression, plot the predicted y value vs actual y values for the test data
plt.scatter(Y_test, y_pred)
Out[140]:
<matplotlib.collections.PathCollection at 0x7f74e9c10fd0>
In [141]:
from sklearn.metrics import r2_score
R2_Sgd_Sale_Pred = r2_score(Y_test, y_pred)
print(R2_Sgd_Sale_Pred)
0.5011674821308041
In [142]:
y_train_pred = sgd_reg.predict(X_train)
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Train data
print(mean_absolute_error(y_train, y_train_pred))
print(mean_squared_error(y_train, y_train_pred))
print(np.sqrt(mean_squared_error(y_train, y_train_pred)))
0.5392771529249275
0.44552923654690063
0.6674797648969597
In [143]:
RMSE_Sgd_Sale_Pred = np.sqrt(mean_squared_error(Y_test, y_pred))
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of simple linear regression model with SGD is : " + str(mean_absolute_error(Y_test, y_pred)))
print("The mean squared error of simple linear regression model with SGD is : " + str(mean_squared_error(Y_test, y_pred)))
print("The root mean squared error of simple linear regression model with SGD is : " + str(RMSE_Sgd_Sale_Pred))
The mean absolute error of simple linear regression model with SGD is : 0.557255351764031
The mean squared error of simple linear regression model with SGD is : 0.49993705317606163
The root mean squared error of simple linear regression model with SGD is : 0.7070622696595128

Model 2 : Decision Tree with and without gridsearch CV

In [144]:
# Criterion : Mean absolute error
In [145]:
dtree = DecisionTreeRegressor(random_state=0, criterion="mae")
dtree_fit = dtree.fit(X_train, y_train)
In [146]:
dtree_scores = cross_val_score(dtree_fit, X_train, y_train, cv = 5)
print("mean cross validation score: {}".format(np.mean(dtree_scores)))
print("score without cv: {}".format(dtree_fit.score(X_train, y_train)))
mean cross validation score: 0.46102147517
score without cv: 1.0
In [147]:
y_pred = dtree_fit.predict(X_test)
In [148]:
# on the test or hold-out set
from sklearn.metrics import r2_score
R2_DT_WoutGCV_Sale_Pred = r2_score(Y_test,y_pred)
print(R2_DT_WoutGCV_Sale_Pred)
print(dtree_fit.score(X_test, Y_test))
0.4291128820755423
0.4291128820755423
In [149]:
final_mae = mean_absolute_error(Y_test, y_pred)
final_mse = mean_squared_error(Y_test, y_pred)
final_rmse = np.sqrt(final_mse)
In [150]:
RMSE_DT_WoutGCV_Sale_Pred = final_rmse
In [151]:
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of decision tree without gridsearchcv is : " + str(final_mae))
print("The mean squared error of decision tree without gridsearchcv is : " + str(final_mse))
print("The root mean squared error of decision tree without gridsearchcv is : " + str(final_rmse))
The mean absolute error of decision tree without gridsearchcv is : 0.4825549478482848
The mean squared error of decision tree without gridsearchcv is : 0.5721511994656449
The root mean squared error of decision tree without gridsearchcv is : 0.756406768521835
In [152]:
scoring = make_scorer(r2_score)
g_cv = GridSearchCV(DecisionTreeRegressor(random_state=0),
              param_grid={'min_samples_split': range(2, 10)},
              scoring=scoring, cv=5, refit=True)

g_cv.fit(X_train, y_train)
g_cv.best_params_

result = g_cv.cv_results_
# print(result)
R2_DT_WithGCV_Sale_Pred = r2_score(Y_test, g_cv.best_estimator_.predict(X_test))
print(R2_DT_WithGCV_Sale_Pred)
0.6164032918669986
In [153]:
y_pred = g_cv.best_estimator_.predict(X_test)
In [154]:
final_mae = mean_absolute_error(Y_test, y_pred)
final_mse = mean_squared_error(Y_test, y_pred)
final_rmse = np.sqrt(final_mse)
In [155]:
RMSE_DT_WithGCV_Sale_Pred=final_rmse
In [156]:
# Print the results of MAE (mean absolute error), MSE (Mean squared Error), RMSE (Root Mean squared error) - Test data
print("The mean absolute error of decision tree with gridsearchcv is : " + str(final_mae))
print("The mean squared error of decision tree with gridsearchcv is : " + str(final_mse))
print("The root mean squared error of decision tree with gridsearchcv is : " + str(final_rmse))
The mean absolute error of decision tree with gridsearchcv is : 0.398571195315775
The mean squared error of decision tree with gridsearchcv is : 0.3844460836098453
The root mean squared error of decision tree with gridsearchcv is : 0.6200371630877017
In [157]:
print("The best params from gridsearchcv are :" + str(g_cv.best_params_))
The best params from gridsearchcv are :{'min_samples_split': 9}
In [158]:
print("The best estimators from gridsearchcv are :" + str(g_cv.best_estimator_))
The best estimators from gridsearchcv are :DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
           max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=1,
           min_samples_split=9, min_weight_fraction_leaf=0.0,
           presort=False, random_state=0, splitter='best')
In [159]:
# Printing the decision tree
dot_data = export_graphviz(dtree_fit, out_file=None)
In [160]:
graph = graphviz.Source(dot_data) 
In [161]:
dot_data = export_graphviz(dtree_fit, out_file=None, 
                         feature_names=X_train.columns,   
                         filled=True, rounded=True,  
                         special_characters=True)
In [162]:
graph = graphviz.Source(dot_data)
graph 
Out[162]:
Tree 0 dcoilwtico ≤ 0.555 mae = 0.798 samples = 871 value = -0.076 1 day_Sunday ≤ 0.5 mae = 0.805 samples = 440 value = 0.317 0->1 True 880 day_Sunday ≤ 0.5 mae = 0.587 samples = 431 value = -0.637 0->880 False 2 month_04 ≤ 0.5 mae = 0.73 samples = 374 value = 0.186 1->2 749 dcoilwtico ≤ -1.405 mae = 0.835 samples = 66 value = 1.44 1->749 3 day_Saturday ≤ 0.5 mae = 0.714 samples = 340 value = 0.277 2->3 682 day_Saturday ≤ 0.5 mae = 0.342 samples = 34 value = -0.748 2->682 4 month_03 ≤ 0.5 mae = 0.623 samples = 283 value = 0.16 3->4 569 month_03 ≤ 0.5 mae = 0.754 samples = 57 value = 1.557 3->569 5 month_02 ≤ 0.5 mae = 0.584 samples = 258 value = 0.271 4->5 520 day_Thursday ≤ 0.5 mae = 0.203 samples = 25 value = -0.761 4->520 6 month_01 ≤ 0.5 mae = 0.534 samples = 237 value = 0.313 5->6 479 dcoilwtico ≤ -0.309 mae = 0.205 samples = 21 value = -0.803 5->479 7 month_05 ≤ 0.5 mae = 0.494 samples = 211 value = 0.392 6->7 428 dcoilwtico ≤ -1.214 mae = 0.413 samples = 26 value = -0.692 6->428 8 day_Wednesday ≤ 0.5 mae = 0.46 samples = 196 value = 0.42 7->8 399 locale_National ≤ 0.5 mae = 0.265 samples = 15 value = -0.541 7->399 9 day_Thursday ≤ 0.5 mae = 0.419 samples = 160 value = 0.324 8->9 328 month_12 ≤ 0.5 mae = 0.363 samples = 36 value = 0.87 8->328 10 month_12 ≤ 0.5 mae = 0.407 samples = 121 value = 0.419 9->10 251 dcoilwtico ≤ -1.699 mae = 0.29 samples = 39 value = -0.009 9->251 11 day_Tuesday ≤ 0.5 mae = 0.339 samples = 98 value = 0.379 10->11 206 locale_National ≤ 0.5 mae = 0.456 samples = 23 value = 0.923 10->206 12 locale_National ≤ 0.5 mae = 0.286 samples = 65 value = 0.421 11->12 141 locale_National ≤ 0.5 mae = 0.331 samples = 33 value = 0.146 11->141 13 dcoilwtico ≤ 0.084 mae = 0.247 samples = 58 value = 0.416 12->13 128 type_Holiday ≤ 0.5 mae = 0.531 samples = 7 value = 0.775 12->128 14 dcoilwtico ≤ -0.015 mae = 0.198 samples = 48 value = 0.399 13->14 109 month_11 ≤ 0.5 mae = 0.432 samples = 10 value = 0.631 13->109 15 locale_name_Guaranda ≤ 0.5 mae = 0.185 samples = 45 value = 0.413 14->15 104 day_Friday ≤ 0.5 mae = 0.053 samples = 3 value = -0.01 14->104 16 dcoilwtico ≤ -0.955 mae = 0.183 samples = 44 value = 0.416 15->16 103 mae = 0.0 samples = 1 value = 0.133 15->103 17 dcoilwtico ≤ -1.068 mae = 0.194 samples = 34 value = 0.427 16->17 84 dcoilwtico ≤ -0.831 mae = 0.129 samples = 10 value = 0.378 16->84 18 day_Friday ≤ 0.5 mae = 0.178 samples = 33 value = 0.426 17->18 83 mae = 0.0 samples = 1 value = 1.147 17->83 19 dcoilwtico ≤ -1.192 mae = 0.204 samples = 15 value = 0.383 18->19 48 month_10 ≤ 0.5 mae = 0.15 samples = 18 value = 0.434 18->48 20 month_08 ≤ 0.5 mae = 0.124 samples = 14 value = 0.386 19->20 47 mae = 0.0 samples = 1 value = -0.929 19->47 21 dcoilwtico ≤ -1.545 mae = 0.067 samples = 10 value = 0.362 20->21 40 dcoilwtico ≤ -1.512 mae = 0.189 samples = 4 value = 0.658 20->40 22 dcoilwtico ≤ -1.641 mae = 0.007 samples = 2 value = 0.47 21->22 25 dcoilwtico ≤ -1.496 mae = 0.052 samples = 8 value = 0.342 21->25 23 mae = 0.0 samples = 1 value = 0.477 22->23 24 mae = 0.0 samples = 1 value = 0.463 22->24 26 mae = 0.0 samples = 1 value = 0.271 25->26 27 dcoilwtico ≤ -1.399 mae = 0.049 samples = 7 value = 0.342 25->27 28 dcoilwtico ≤ -1.414 mae = 0.042 samples = 4 value = 0.386 27->28 35 month_10 ≤ 0.5 mae = 0.021 samples = 3 value = 0.313 27->35 29 type_No_Holiday ≤ 0.5 mae = 0.015 samples = 3 value = 0.383 28->29 34 mae = 0.0 samples = 1 value = 0.505 28->34 30 mae = 0.0 samples = 1 value = 0.342 29->30 31 month_10 ≤ 0.5 mae = 0.003 samples = 2 value = 0.386 29->31 32 mae = 0.0 samples = 1 value = 0.383 31->32 33 mae = 0.0 samples = 1 value = 0.388 31->33 36 dcoilwtico ≤ -1.312 mae = 0.014 samples = 2 value = 0.327 35->36 39 mae = 0.0 samples = 1 value = 0.277 35->39 37 mae = 0.0 samples = 1 value = 0.313 36->37 38 mae = 0.0 samples = 1 value = 0.341 36->38 41 transferred_False ≤ 0.5 mae = 0.11 samples = 2 value = 0.43 40->41 44 dcoilwtico ≤ -1.361 mae = 0.032 samples = 2 value = 0.808 40->44 42 mae = 0.0 samples = 1 value = 0.32 41->42 43 mae = 0.0 samples = 1 value = 0.54 41->43 45 mae = 0.0 samples = 1 value = 0.777 44->45 46 mae = 0.0 samples = 1 value = 0.84 44->46 49 dcoilwtico ≤ -1.42 mae = 0.136 samples = 15 value = 0.428 48->49 78 dcoilwtico ≤ -1.372 mae = 0.149 samples = 3 value = 0.648 48->78 50 locale_name_None ≤ 0.5 mae = 0.113 samples = 10 value = 0.422 49->50 69 dcoilwtico ≤ -1.388 mae = 0.153 samples = 5 value = 0.567 49->69 51 mae = 0.0 samples = 1 value = 0.485 50->51 52 dcoilwtico ≤ -1.436 mae = 0.118 samples = 9 value = 0.419 50->52 53 dcoilwtico ≤ -1.664 mae = 0.126 samples = 8 value = 0.418 52->53 68 mae = 0.0 samples = 1 value = 0.478 52->68 54 mae = 0.0 samples = 1 value = 0.441 53->54 55 month_11 ≤ 0.5 mae = 0.14 samples = 7 value = 0.418 53->55 56 dcoilwtico ≤ -1.529 mae = 0.157 samples = 6 value = 0.418 55->56 67 mae = 0.0 samples = 1 value = 0.376 55->67 57 dcoilwtico ≤ -1.6 mae = 0.385 samples = 2 value = 0.811 56->57 60 dcoilwtico ≤ -1.482 mae = 0.039 samples = 4 value = 0.412 56->60 58 mae = 0.0 samples = 1 value = 0.426 57->58 59 mae = 0.0 samples = 1 value = 1.196 57->59 61 mae = 0.0 samples = 1 value = 0.276 60->61 62 dcoilwtico ≤ -1.464 mae = 0.004 samples = 3 value = 0.418 60->62 63 mae = 0.0 samples = 1 value = 0.406 62->63 64 month_09 ≤ 0.5 mae = 0.0 samples = 2 value = 0.418 62->64 65 mae = 0.0 samples = 1 value = 0.419 64->65 66 mae = 0.0 samples = 1 value = 0.418 64->66 70 mae = 0.0 samples = 1 value = 0.93 69->70 71 transferred_None ≤ 0.5 mae = 0.1 samples = 4 value = 0.498 69->71 72 mae = 0.0 samples = 1 value = 0.312 71->72 73 dcoilwtico ≤ -1.17 mae = 0.049 samples = 3 value = 0.567 71->73 74 dcoilwtico ≤ -1.287 mae = 0.004 samples = 2 value = 0.571 73->74 77 mae = 0.0 samples = 1 value = 0.428 73->77 75 mae = 0.0 samples = 1 value = 0.574 74->75 76 mae = 0.0 samples = 1 value = 0.567 74->76 79 dcoilwtico ≤ -1.442 mae = 0.185 samples = 2 value = 0.463 78->79 82 mae = 0.0 samples = 1 value = 0.726 78->82 80 mae = 0.0 samples = 1 value = 0.648 79->80 81 mae = 0.0 samples = 1 value = 0.278 79->81 85 dcoilwtico ≤ -0.903 mae = 0.09 samples = 5 value = 0.272 84->85 94 month_11 ≤ 0.5 mae = 0.131 samples = 5 value = 0.413 84->94 86 mae = 0.0 samples = 1 value = 0.384 85->86 87 dcoilwtico ≤ -0.883 mae = 0.084 samples = 4 value = 0.213 85->87 88 mae = 0.0 samples = 1 value = 0.155 87->88 89 dcoilwtico ≤ -0.861 mae = 0.073 samples = 3 value = 0.272 87->89 90 mae = 0.0 samples = 1 value = 0.372 89->90 91 dcoilwtico ≤ -0.843 mae = 0.06 samples = 2 value = 0.212 89->91 92 mae = 0.0 samples = 1 value = 0.152 91->92 93 mae = 0.0 samples = 1 value = 0.272 91->93 95 dcoilwtico ≤ -0.825 mae = 0.182 samples = 2 value = 0.602 94->95 98 day_Monday ≤ 0.5 mae = 0.086 samples = 3 value = 0.392 94->98 96 mae = 0.0 samples = 1 value = 0.421 95->96 97 mae = 0.0 samples = 1 value = 0.784 95->97 99 dcoilwtico ≤ -0.111 mae = 0.01 samples = 2 value = 0.403 98->99 102 mae = 0.0 samples = 1 value = 0.156 98->102 100 mae = 0.0 samples = 1 value = 0.392 99->100 101 mae = 0.0 samples = 1 value = 0.413 99->101 105 mae = 0.0 samples = 1 value = 0.098 104->105 106 dcoilwtico ≤ 0.038 mae = 0.026 samples = 2 value = -0.036 104->106 107 mae = 0.0 samples = 1 value = -0.061 106->107 108 mae = 0.0 samples = 1 value = -0.01 106->108 110 day_Friday ≤ 0.5 mae = 0.155 samples = 8 value = 0.706 109->110 125 day_Friday ≤ 0.5 mae = 0.01 samples = 2 value = -0.836 109->125 111 dcoilwtico ≤ 0.506 mae = 0.093 samples = 4 value = 0.527 110->111 118 dcoilwtico ≤ 0.262 mae = 0.141 samples = 4 value = 0.766 110->118 112 dcoilwtico ≤ 0.273 mae = 0.054 samples = 3 value = 0.497 111->112 117 mae = 0.0 samples = 1 value = 0.704 111->117 113 mae = 0.0 samples = 1 value = 0.558 112->113 114 month_10 ≤ 0.5 mae = 0.051 samples = 2 value = 0.446 112->114 115 mae = 0.0 samples = 1 value = 0.497 114->115 116 mae = 0.0 samples = 1 value = 0.395 114->116 119 mae = 0.0 samples = 1 value = 0.708 118->119 120 month_10 ≤ 0.5 mae = 0.149 samples = 3 value = 0.824 118->120 121 dcoilwtico ≤ 0.53 mae = 0.058 samples = 2 value = 0.766 120->121 124 mae = 0.0 samples = 1 value = 1.155 120->124 122 mae = 0.0 samples = 1 value = 0.708 121->122 123 mae = 0.0 samples = 1 value = 0.824 121->123 126 mae = 0.0 samples = 1 value = -0.845 125->126 127 mae = 0.0 samples = 1 value = -0.826 125->127 129 dcoilwtico ≤ -1.11 mae = 0.112 samples = 3 value = 0.436 128->129 134 dcoilwtico ≤ -1.333 mae = 0.493 samples = 4 value = 1.352 128->134 130 day_Friday ≤ 0.5 mae = 0.033 samples = 2 value = 0.469 129->130 133 mae = 0.0 samples = 1 value = 0.167 129->133 131 mae = 0.0 samples = 1 value = 0.503 130->131 132 mae = 0.0 samples = 1 value = 0.436 130->132 135 dcoilwtico ≤ -1.431 mae = 0.133 samples = 2 value = 0.908 134->135 138 month_10 ≤ 0.5 mae = 0.231 samples = 2 value = 1.893 134->138 136 mae = 0.0 samples = 1 value = 1.041 135->136 137 mae = 0.0 samples = 1 value = 0.775 135->137 139 mae = 0.0 samples = 1 value = 2.124 138->139 140 mae = 0.0 samples = 1 value = 1.662 138->140 142 locale_name_Ambato ≤ 0.5 mae = 0.285 samples = 32 value = 0.142 141->142 205 mae = 0.0 samples = 1 value = 1.946 141->205 143 dcoilwtico ≤ 0.533 mae = 0.236 samples = 31 value = 0.139 142->143 204 mae = 0.0 samples = 1 value = 1.942 142->204 144 month_07 ≤ 0.5 mae = 0.218 samples = 30 value = 0.116 143->144 203 mae = 0.0 samples = 1 value = 0.903 143->203 145 dcoilwtico ≤ 0.065 mae = 0.211 samples = 27 value = 0.146 144->145 198 dcoilwtico ≤ -1.275 mae = 0.04 samples = 3 value = -0.112 144->198 146 dcoilwtico ≤ -0.016 mae = 0.239 samples = 22 value = 0.086 145->146 189 locale_name_Quevedo ≤ 0.5 mae = 0.031 samples = 5 value = 0.21 145->189 147 month_11 ≤ 0.5 mae = 0.235 samples = 21 value = 0.094 146->147 188 mae = 0.0 samples = 1 value = -0.22 146->188 148 dcoilwtico ≤ -0.781 mae = 0.288 samples = 16 value = 0.144 147->148 179 locale_name_None ≤ 0.5 mae = 0.04 samples = 5 value = 0.039 147->179 149 dcoilwtico ≤ -1.42 mae = 0.288 samples = 15 value = 0.139 148->149 178 mae = 0.0 samples = 1 value = 0.419 148->178 150 dcoilwtico ≤ -1.542 mae = 0.371 samples = 7 value = 0.236 149->150 163 dcoilwtico ≤ -1.344 mae = 0.188 samples = 8 value = 0.076 149->163 151 dcoilwtico ≤ -1.625 mae = 0.068 samples = 2 value = 0.071 150->151 154 dcoilwtico ≤ -1.43 mae = 0.344 samples = 5 value = 0.78 150->154 152 mae = 0.0 samples = 1 value = 0.139 151->152 153 mae = 0.0 samples = 1 value = 0.003 151->153 155 dcoilwtico ≤ -1.454 mae = 0.295 samples = 4 value = 0.866 154->155 162 mae = 0.0 samples = 1 value = 0.236 154->162 156 month_09 ≤ 0.5 mae = 0.312 samples = 3 value = 0.78 155->156 161 mae = 0.0 samples = 1 value = 1.023 155->161 157 month_10 ≤ 0.5 mae = 0.086 samples = 2 value = 0.866 156->157 160 mae = 0.0 samples = 1 value = 0.017 156->160 158 mae = 0.0 samples = 1 value = 0.78 157->158 159 mae = 0.0 samples = 1 value = 0.952 157->159 164 dcoilwtico ≤ -1.393 mae = 0.257 samples = 4 value = -0.016 163->164 171 transferred_None ≤ 0.5 mae = 0.054 samples = 4 value = 0.172 163->171 165 month_10 ≤ 0.5 mae = 0.042 samples = 3 value = 0.018 164->165 170 mae = 0.0 samples = 1 value = -0.881 164->170 166 dcoilwtico ≤ -1.41 mae = 0.03 samples = 2 value = 0.048 165->166 169 mae = 0.0 samples = 1 value = -0.05 165->169 167 mae = 0.0 samples = 1 value = 0.018 166->167 168 mae = 0.0 samples = 1 value = 0.077 166->168 172 mae = 0.0 samples = 1 value = 0.074 171->172 173 dcoilwtico ≤ -1.068 mae = 0.031 samples = 3 value = 0.194 171->173 174 mae = 0.0 samples = 1 value = 0.244 173->174 175 dcoilwtico ≤ -0.827 mae = 0.022 samples = 2 value = 0.172 173->175 176 mae = 0.0 samples = 1 value = 0.15 175->176 177 mae = 0.0 samples = 1 value = 0.194 175->177 180 locale_name_Guaranda ≤ 0.5 mae = 0.026 samples = 2 value = 0.12 179->180 183 dcoilwtico ≤ -0.935 mae = 0.012 samples = 3 value = 0.038 179->183 181 mae = 0.0 samples = 1 value = 0.146 180->181 182 mae = 0.0 samples = 1 value = 0.094 180->182 184 mae = 0.0 samples = 1 value = 0.039 183->184 185 dcoilwtico ≤ -0.236 mae = 0.018 samples = 2 value = 0.02 183->185 186 mae = 0.0 samples = 1 value = 0.003 185->186 187 mae = 0.0 samples = 1 value = 0.038 185->187 190 dcoilwtico ≤ 0.104 mae = 0.023 samples = 4 value = 0.198 189->190 197 mae = 0.0 samples = 1 value = 0.271 189->197 191 mae = 0.0 samples = 1 value = 0.16 190->191 192 dcoilwtico ≤ 0.497 mae = 0.014 samples = 3 value = 0.21 190->192 193 dcoilwtico ≤ 0.3 mae = 0.008 samples = 2 value = 0.219 192->193 196 mae = 0.0 samples = 1 value = 0.185 192->196 194 mae = 0.0 samples = 1 value = 0.21 193->194 195 mae = 0.0 samples = 1 value = 0.227 193->195 199 mae = 0.0 samples = 1 value = -0.217 198->199 200 dcoilwtico ≤ -1.17 mae = 0.007 samples = 2 value = -0.105 198->200 201 mae = 0.0 samples = 1 value = -0.112 200->201 202 mae = 0.0 samples = 1 value = -0.097 200->202 207 dcoilwtico ≤ -1.087 mae = 0.381 samples = 17 value = 0.732 206->207 240 dcoilwtico ≤ -1.438 mae = 0.358 samples = 6 value = 1.509 206->240 208 dcoilwtico ≤ -1.813 mae = 0.288 samples = 9 value = 0.637 207->208 225 dcoilwtico ≤ -0.63 mae = 0.405 samples = 8 value = 0.921 207->225 209 dcoilwtico ≤ -1.861 mae = 0.152 samples = 3 value = 0.965 208->209 214 dcoilwtico ≤ -1.649 mae = 0.193 samples = 6 value = 0.435 208->214 210 mae = 0.0 samples = 1 value = 1.344 209->210 211 dcoilwtico ≤ -1.828 mae = 0.039 samples = 2 value = 0.927 209->211 212 mae = 0.0 samples = 1 value = 0.888 211->212 213 mae = 0.0 samples = 1 value = 0.965 211->213 215 day_Friday ≤ 0.5 mae = 0.142 samples = 4 value = 0.304 214->215 222 dcoilwtico ≤ -1.369 mae = 0.036 samples = 2 value = 0.601 214->222 216 day_Monday ≤ 0.5 mae = 0.115 samples = 3 value = 0.305 215->216 221 mae = 0.0 samples = 1 value = 0.08 215->221 217 transferred_False ≤ 0.5 mae = 0.171 samples = 2 value = 0.476 216->217 220 mae = 0.0 samples = 1 value = 0.303 216->220 218 mae = 0.0 samples = 1 value = 0.647 217->218 219 mae = 0.0 samples = 1 value = 0.305 217->219 223 mae = 0.0 samples = 1 value = 0.637 222->223 224 mae = 0.0 samples = 1 value = 0.565 222->224 226 day_Tuesday ≤ 0.5 mae = 0.446 samples = 6 value = 0.979 225->226 237 locale_Local ≤ 0.5 mae = 0.045 samples = 2 value = 0.641 225->237 227 dcoilwtico ≤ -0.94 mae = 0.101 samples = 3 value = 0.92 226->227 232 dcoilwtico ≤ -1.035 mae = 0.671 samples = 3 value = 1.277 226->232 228 day_Monday ≤ 0.5 mae = 0.057 samples = 2 value = 0.977 227->228 231 mae = 0.0 samples = 1 value = 0.732 227->231 229 mae = 0.0 samples = 1 value = 0.92 228->229 230 mae = 0.0 samples = 1 value = 1.035 228->230 233 mae = 0.0 samples = 1 value = 0.923 232->233 234 dcoilwtico ≤ -0.836 mae = 0.829 samples = 2 value = 2.106 232->234 235 mae = 0.0 samples = 1 value = 2.936 234->235 236 mae = 0.0 samples = 1 value = 1.277 234->236 238 mae = 0.0 samples = 1 value = 0.686 237->238 239 mae = 0.0 samples = 1 value = 0.595 237->239 241 day_Monday ≤ 0.5 mae = 0.207 samples = 2 value = 2.013 240->241 244 day_Friday ≤ 0.5 mae = 0.192 samples = 4 value = 1.274 240->244 242 mae = 0.0 samples = 1 value = 1.805 241->242 243 mae = 0.0 samples = 1 value = 2.22 241->243 245 dcoilwtico ≤ -0.995 mae = 0.156 samples = 3 value = 1.325 244->245 250 mae = 0.0 samples = 1 value = 1.023 244->250 246 mae = 0.0 samples = 1 value = 1.693 245->246 247 day_Tuesday ≤ 0.5 mae = 0.05 samples = 2 value = 1.274 245->247 248 mae = 0.0 samples = 1 value = 1.224 247->248 249 mae = 0.0 samples = 1 value = 1.325 247->249 252 dcoilwtico ≤ -1.77 mae = 0.124 samples = 3 value = 0.601 251->252 257 locale_name_Ambato ≤ 0.5 mae = 0.258 samples = 36 value = -0.032 251->257 253 locale_name_Ecuador ≤ 0.5 mae = 0.053 samples = 2 value = 0.548 252->253 256 mae = 0.0 samples = 1 value = 0.866 252->256 254 mae = 0.0 samples = 1 value = 0.495 253->254 255 mae = 0.0 samples = 1 value = 0.601 253->255 258 month_06 ≤ 0.5 mae = 0.241 samples = 35 value = -0.026 257->258 327 mae = 0.0 samples = 1 value = -0.864 257->327 259 month_12 ≤ 0.5 mae = 0.228 samples = 31 value = -0.009 258->259 320 dcoilwtico ≤ -0.88 mae = 0.177 samples = 4 value = -0.381 258->320 260 locale_Local ≤ 0.5 mae = 0.226 samples = 29 value = -0.026 259->260 317 dcoilwtico ≤ -0.69 mae = 0.088 samples = 2 value = 0.24 259->317 261 month_08 ≤ 0.5 mae = 0.226 samples = 28 value = -0.018 260->261 316 mae = 0.0 samples = 1 value = -0.254 260->316 262 dcoilwtico ≤ -1.456 mae = 0.236 samples = 25 value = -0.0 261->262 311 dcoilwtico ≤ -1.589 mae = 0.085 samples = 3 value = -0.132 261->311 263 month_11 ≤ 0.5 mae = 0.196 samples = 4 value = 0.222 262->263 270 month_11 ≤ 0.5 mae = 0.231 samples = 21 value = -0.009 262->270 264 dcoilwtico ≤ -1.46 mae = 0.12 samples = 2 value = 0.437 263->264 267 dcoilwtico ≤ -1.631 mae = 0.083 samples = 2 value = 0.045 263->267 265 mae = 0.0 samples = 1 value = 0.557 264->265 266 mae = 0.0 samples = 1 value = 0.317 264->266 268 mae = 0.0 samples = 1 value = 0.127 267->268 269 mae = 0.0 samples = 1 value = -0.038 267->269 271 dcoilwtico ≤ -1.169 mae = 0.192 samples = 16 value = -0.005 270->271 302 dcoilwtico ≤ 0.212 mae = 0.323 samples = 5 value = -0.166 270->302 272 dcoilwtico ≤ -1.34 mae = 0.181 samples = 8 value = -0.018 271->272 287 dcoilwtico ≤ 0.107 mae = 0.194 samples = 8 value = 0.103 271->287 273 dcoilwtico ≤ -1.403 mae = 0.173 samples = 5 value = -0.0 272->273 282 dcoilwtico ≤ -1.238 mae = 0.1 samples = 3 value = -0.242 272->282 274 dcoilwtico ≤ -1.433 mae = 0.228 samples = 2 value = -0.237 273->274 277 dcoilwtico ≤ -1.377 mae = 0.13 samples = 3 value = 0.003 273->277 275 mae = 0.0 samples = 1 value = -0.009 274->275 276 mae = 0.0 samples = 1 value = -0.465 274->276 278 dcoilwtico ≤ -1.388 mae = 0.193 samples = 2 value = 0.196 277->278 281 mae = 0.0 samples = 1 value = -0.0 277->281 279 mae = 0.0 samples = 1 value = 0.003 278->279 280 mae = 0.0 samples = 1 value = 0.389 278->280 283 month_10 ≤ 0.5 mae = 0.042 samples = 2 value = -0.285 282->283 286 mae = 0.0 samples = 1 value = -0.026 282->286 284 mae = 0.0 samples = 1 value = -0.327 283->284 285 mae = 0.0 samples = 1 value = -0.242 283->285 288 dcoilwtico ≤ -1.044 mae = 0.039 samples = 3 value = 0.27 287->288 293 dcoilwtico ≤ 0.179 mae = 0.164 samples = 5 value = -0.051 287->293 289 mae = 0.0 samples = 1 value = 0.177 288->289 290 month_10 ≤ 0.5 mae = 0.011 samples = 2 value = 0.281 288->290 291 mae = 0.0 samples = 1 value = 0.27 290->291 292 mae = 0.0 samples = 1 value = 0.293 290->292 294 mae = 0.0 samples = 1 value = -0.239 293->294 295 transferred_True ≤ 0.5 mae = 0.158 samples = 4 value = -0.011 293->295 296 dcoilwtico ≤ 0.497 mae = 0.184 samples = 3 value = 0.029 295->296 301 mae = 0.0 samples = 1 value = -0.051 295->301 297 mae = 0.0 samples = 1 value = 0.499 296->297 298 dcoilwtico ≤ 0.54 mae = 0.041 samples = 2 value = -0.012 296->298 299 mae = 0.0 samples = 1 value = -0.053 298->299 300 mae = 0.0 samples = 1 value = 0.029 298->300 303 transferred_None ≤ 0.5 mae = 0.14 samples = 4 value = -0.039 302->303 310 mae = 0.0 samples = 1 value = -1.218 302->310 304 mae = 0.0 samples = 1 value = 0.098 303->304 305 dcoilwtico ≤ -0.333 mae = 0.099 samples = 3 value = -0.166 303->305 306 mae = 0.0 samples = 1 value = 0.088 305->306 307 dcoilwtico ≤ -0.212 mae = 0.021 samples = 2 value = -0.187 305->307 308 mae = 0.0 samples = 1 value = -0.208 307->308 309 mae = 0.0 samples = 1 value = -0.166 307->309 312 mae = 0.0 samples = 1 value = -0.293 311->312 313 dcoilwtico ≤ -1.512 mae = 0.047 samples = 2 value = -0.085 311->313 314 mae = 0.0 samples = 1 value = -0.037 313->314 315 mae = 0.0 samples = 1 value = -0.132 313->315 318 mae = 0.0 samples = 1 value = 0.329 317->318 319 mae = 0.0 samples = 1 value = 0.152 317->319 321 mae = 0.0 samples = 1 value = 0.034 320->321 322 dcoilwtico ≤ -0.83 mae = 0.086 samples = 3 value = -0.414 320->322 323 mae = 0.0 samples = 1 value = -0.608 322->323 324 dcoilwtico ≤ -0.806 mae = 0.032 samples = 2 value = -0.381 322->324 325 mae = 0.0 samples = 1 value = -0.349 324->325 326 mae = 0.0 samples = 1 value = -0.414 324->326 329 dcoilwtico ≤ 0.494 mae = 0.276 samples = 30 value = 0.847 328->329 388 dcoilwtico ≤ -0.753 mae = 0.39 samples = 6 value = 1.666 328->388 330 dcoilwtico ≤ -0.047 mae = 0.228 samples = 29 value = 0.85 329->330 387 mae = 0.0 samples = 1 value = -0.805 329->387 331 dcoilwtico ≤ -0.802 mae = 0.166 samples = 24 value = 0.832 330->331 378 dcoilwtico ≤ 0.199 mae = 0.371 samples = 5 value = 1.123 330->378 332 month_08 ≤ 0.5 mae = 0.154 samples = 21 value = 0.85 331->332 373 month_11 ≤ 0.5 mae = 0.067 samples = 3 value = 0.643 331->373 333 dcoilwtico ≤ -1.183 mae = 0.145 samples = 17 value = 0.872 332->333 366 dcoilwtico ≤ -1.676 mae = 0.096 samples = 4 value = 0.695 332->366 334 dcoilwtico ≤ -1.331 mae = 0.106 samples = 12 value = 0.86 333->334 357 month_07 ≤ 0.5 mae = 0.183 samples = 5 value = 0.982 333->357 335 dcoilwtico ≤ -1.421 mae = 0.083 samples = 9 value = 0.872 334->335 352 dcoilwtico ≤ -1.231 mae = 0.093 samples = 3 value = 0.664 334->352 336 month_10 ≤ 0.5 mae = 0.042 samples = 6 value = 0.857 335->336 347 month_10 ≤ 0.5 mae = 0.084 samples = 3 value = 1.037 335->347 337 type_Holiday ≤ 0.5 mae = 0.036 samples = 5 value = 0.869 336->337 346 mae = 0.0 samples = 1 value = 0.793 336->346 338 dcoilwtico ≤ -1.47 mae = 0.032 samples = 4 value = 0.87 337->338 345 mae = 0.0 samples = 1 value = 0.819 337->345 339 dcoilwtico ≤ -1.62 mae = 0.033 samples = 3 value = 0.872 338->339 344 mae = 0.0 samples = 1 value = 0.844 338->344 340 mae = 0.0 samples = 1 value = 0.869 339->340 341 dcoilwtico ≤ -1.55 mae = 0.049 samples = 2 value = 0.92 339->341 342 mae = 0.0 samples = 1 value = 0.969 341->342 343 mae = 0.0 samples = 1 value = 0.872 341->343 348 month_09 ≤ 0.5 mae = 0.061 samples = 2 value = 1.098 347->348 351 mae = 0.0 samples = 1 value = 0.905 347->351 349 mae = 0.0 samples = 1 value = 1.159 348->349 350 mae = 0.0 samples = 1 value = 1.037 348->350 353 dcoilwtico ≤ -1.286 mae = 0.047 samples = 2 value = 0.617 352->353 356 mae = 0.0 samples = 1 value = 0.85 352->356 354 mae = 0.0 samples = 1 value = 0.57 353->354 355 mae = 0.0 samples = 1 value = 0.664 353->355 358 dcoilwtico ≤ -0.832 mae = 0.055 samples = 3 value = 0.95 357->358 363 dcoilwtico ≤ -1.068 mae = 0.119 samples = 2 value = 1.341 357->363 359 dcoilwtico ≤ -0.839 mae = 0.016 samples = 2 value = 0.966 358->359 362 mae = 0.0 samples = 1 value = 0.816 358->362 360 mae = 0.0 samples = 1 value = 0.982 359->360 361 mae = 0.0 samples = 1 value = 0.95 359->361 364 mae = 0.0 samples = 1 value = 1.222 363->364 365 mae = 0.0 samples = 1 value = 1.46 363->365 367 mae = 0.0 samples = 1 value = 0.489 366->367 368 dcoilwtico ≤ -1.578 mae = 0.047 samples = 3 value = 0.732 366->368 369 mae = 0.0 samples = 1 value = 0.658 368->369 370 locale_name_Esmeraldas ≤ 0.5 mae = 0.033 samples = 2 value = 0.765 368->370 371 mae = 0.0 samples = 1 value = 0.732 370->371 372 mae = 0.0 samples = 1 value = 0.798 370->372 374 mae = 0.0 samples = 1 value = 0.476 373->374 375 dcoilwtico ≤ -0.171 mae = 0.016 samples = 2 value = 0.659 373->375 376 mae = 0.0 samples = 1 value = 0.676 375->376 377 mae = 0.0 samples = 1 value = 0.643 375->377 379 dcoilwtico ≤ 0.085 mae = 0.241 samples = 3 value = 1.098 378->379 384 month_10 ≤ 0.5 mae = 0.469 samples = 2 value = 1.676 378->384 380 dcoilwtico ≤ 0.049 mae = 0.013 samples = 2 value = 1.11 379->380 383 mae = 0.0 samples = 1 value = 0.399 379->383 381 mae = 0.0 samples = 1 value = 1.098 380->381 382 mae = 0.0 samples = 1 value = 1.123 380->382 385 mae = 0.0 samples = 1 value = 1.207 384->385 386 mae = 0.0 samples = 1 value = 2.146 384->386 389 dcoilwtico ≤ -1.04 mae = 0.39 samples = 5 value = 1.677 388->389 398 mae = 0.0 samples = 1 value = 1.284 388->398 390 dcoilwtico ≤ -1.383 mae = 0.479 samples = 4 value = 1.666 389->390 397 mae = 0.0 samples = 1 value = 1.71 389->397 391 type_Additional ≤ 0.5 mae = 0.254 samples = 3 value = 1.677 390->391 396 mae = 0.0 samples = 1 value = 0.522 390->396 392 dcoilwtico ≤ -1.733 mae = 0.011 samples = 2 value = 1.666 391->392 395 mae = 0.0 samples = 1 value = 2.415 391->395 393 mae = 0.0 samples = 1 value = 1.677 392->393 394 mae = 0.0 samples = 1 value = 1.655 392->394 400 day_Thursday ≤ 0.5 mae = 0.195 samples = 13 value = -0.594 399->400 425 dcoilwtico ≤ -0.21 mae = 0.146 samples = 2 value = 0.15 399->425 401 dcoilwtico ≤ -0.123 mae = 0.173 samples = 11 value = -0.541 400->401 422 dcoilwtico ≤ -0.874 mae = 0.01 samples = 2 value = -0.886 400->422 402 day_Tuesday ≤ 0.5 mae = 0.14 samples = 10 value = -0.514 401->402 421 mae = 0.0 samples = 1 value = -1.044 401->421 403 dcoilwtico ≤ -0.905 mae = 0.096 samples = 8 value = -0.469 402->403 418 type_No_Holiday ≤ 0.5 mae = 0.071 samples = 2 value = -0.802 402->418 404 mae = 0.0 samples = 1 value = -0.35 403->404 405 dcoilwtico ≤ -0.869 mae = 0.09 samples = 7 value = -0.488 403->405 406 mae = 0.0 samples = 1 value = -0.541 405->406 407 day_Friday ≤ 0.5 mae = 0.096 samples = 6 value = -0.469 405->407 408 dcoilwtico ≤ -0.858 mae = 0.076 samples = 4 value = -0.541 407->408 415 dcoilwtico ≤ -0.837 mae = 0.097 samples = 2 value = -0.351 407->415 409 mae = 0.0 samples = 1 value = -0.45 408->409 410 dcoilwtico ≤ -0.8 mae = 0.053 samples = 3 value = -0.594 408->410 411 dcoilwtico ≤ -0.831 mae = 0.026 samples = 2 value = -0.62 410->411 414 mae = 0.0 samples = 1 value = -0.488 410->414 412 mae = 0.0 samples = 1 value = -0.594 411->412 413 mae = 0.0 samples = 1 value = -0.646 411->413 416 mae = 0.0 samples = 1 value = -0.448 415->416 417 mae = 0.0 samples = 1 value = -0.254 415->417 419 mae = 0.0 samples = 1 value = -0.873 418->419 420 mae = 0.0 samples = 1 value = -0.731 418->420 423 mae = 0.0 samples = 1 value = -0.896 422->423 424 mae = 0.0 samples = 1 value = -0.876 422->424 426 mae = 0.0 samples = 1 value = 0.296 425->426 427 mae = 0.0 samples = 1 value = 0.004 425->427 429 dcoilwtico ≤ -1.412 mae = 0.141 samples = 14 value = -0.71 428->429 456 day_Wednesday ≤ 0.5 mae = 0.419 samples = 12 value = 0.08 428->456 430 day_Friday ≤ 0.5 mae = 0.131 samples = 5 value = -0.946 429->430 439 dcoilwtico ≤ -1.37 mae = 0.103 samples = 9 value = -0.7 429->439 431 day_Wednesday ≤ 0.5 mae = 0.104 samples = 4 value = -0.998 430->431 438 mae = 0.0 samples = 1 value = -0.709 430->438 432 dcoilwtico ≤ -1.416 mae = 0.048 samples = 3 value = -1.049 431->432 437 mae = 0.0 samples = 1 value = -0.777 431->437 433 dcoilwtico ≤ -1.454 mae = 0.021 samples = 2 value = -1.07 432->433 436 mae = 0.0 samples = 1 value = -0.946 432->436 434 mae = 0.0 samples = 1 value = -1.049 433->434 435 mae = 0.0 samples = 1 value = -1.091 433->435 440 day_Tuesday ≤ 0.5 mae = 0.094 samples = 2 value = -0.805 439->440 443 day_Monday ≤ 0.5 mae = 0.1 samples = 7 value = -0.684 439->443 441 mae = 0.0 samples = 1 value = -0.711 440->441 442 mae = 0.0 samples = 1 value = -0.899 440->442 444 day_Wednesday ≤ 0.5 mae = 0.111 samples = 6 value = -0.692 443->444 455 mae = 0.0 samples = 1 value = -0.649 443->455 445 day_Tuesday ≤ 0.5 mae = 0.044 samples = 4 value = -0.701 444->445 452 dcoilwtico ≤ -1.317 mae = 0.23 samples = 2 value = -0.455 444->452 446 dcoilwtico ≤ -1.311 mae = 0.058 samples = 3 value = -0.7 445->446 451 mae = 0.0 samples = 1 value = -0.702 445->451 447 mae = 0.0 samples = 1 value = -0.541 446->447 448 day_Thursday ≤ 0.5 mae = 0.007 samples = 2 value = -0.708 446->448 449 mae = 0.0 samples = 1 value = -0.715 448->449 450 mae = 0.0 samples = 1 value = -0.7 448->450 453 mae = 0.0 samples = 1 value = -0.684 452->453 454 mae = 0.0 samples = 1 value = -0.225 452->454 457 day_Thursday ≤ 0.5 mae = 0.365 samples = 11 value = 0.069 456->457 478 mae = 0.0 samples = 1 value = 1.092 456->478 458 day_Monday ≤ 0.5 mae = 0.325 samples = 9 value = 0.092 457->458 475 dcoilwtico ≤ 0.506 mae = 0.425 samples = 2 value = -0.462 457->475 459 dcoilwtico ≤ 0.51 mae = 0.389 samples = 7 value = 0.069 458->459 472 dcoilwtico ≤ 0.511 mae = 0.05 samples = 2 value = 0.183 458->472 460 locale_name_None ≤ 0.5 mae = 0.174 samples = 2 value = -0.104 459->460 463 dcoilwtico ≤ 0.53 mae = 0.47 samples = 5 value = 0.092 459->463 461 mae = 0.0 samples = 1 value = 0.069 460->461 462 mae = 0.0 samples = 1 value = -0.278 460->462 464 mae = 0.0 samples = 1 value = 0.279 463->464 465 day_Tuesday ≤ 0.5 mae = 0.541 samples = 4 value = -0.31 463->465 466 mae = 0.0 samples = 1 value = -0.712 465->466 467 dcoilwtico ≤ 0.55 mae = 0.453 samples = 3 value = 0.092 465->467 468 mae = 0.0 samples = 1 value = 0.122 467->468 469 dcoilwtico ≤ 0.552 mae = 0.664 samples = 2 value = -0.573 467->469 470 mae = 0.0 samples = 1 value = -1.237 469->470 471 mae = 0.0 samples = 1 value = 0.092 469->471 473 mae = 0.0 samples = 1 value = 0.133 472->473 474 mae = 0.0 samples = 1 value = 0.233 472->474 476 mae = 0.0 samples = 1 value = -0.037 475->476 477 mae = 0.0 samples = 1 value = -0.888 475->477 480 day_Thursday ≤ 0.5 mae = 0.158 samples = 17 value = -0.751 479->480 513 day_Monday ≤ 0.5 mae = 0.114 samples = 4 value = -1.252 479->513 481 dcoilwtico ≤ -1.309 mae = 0.119 samples = 13 value = -0.714 480->481 506 dcoilwtico ≤ -1.284 mae = 0.087 samples = 4 value = -1.009 480->506 482 mae = 0.0 samples = 1 value = -0.507 481->482 483 day_Friday ≤ 0.5 mae = 0.112 samples = 12 value = -0.732 481->483 484 dcoilwtico ≤ -1.196 mae = 0.102 samples = 9 value = -0.751 483->484 501 dcoilwtico ≤ -1.156 mae = 0.073 samples = 3 value = -0.627 483->501 485 dcoilwtico ≤ -1.27 mae = 0.095 samples = 6 value = -0.802 484->485 496 dcoilwtico ≤ -1.139 mae = 0.063 samples = 3 value = -0.692 484->496 486 day_Monday ≤ 0.5 mae = 0.072 samples = 3 value = -0.801 485->486 491 day_Tuesday ≤ 0.5 mae = 0.096 samples = 3 value = -0.868 485->491 487 dcoilwtico ≤ -1.302 mae = 0.001 samples = 2 value = -0.802 486->487 490 mae = 0.0 samples = 1 value = -0.587 486->490 488 mae = 0.0 samples = 1 value = -0.801 487->488 489 mae = 0.0 samples = 1 value = -0.803 487->489 492 dcoilwtico ≤ -1.249 mae = 0.059 samples = 2 value = -0.809 491->492 495 mae = 0.0 samples = 1 value = -1.04 491->495 493 mae = 0.0 samples = 1 value = -0.868 492->493 494 mae = 0.0 samples = 1 value = -0.751 492->494 497 mae = 0.0 samples = 1 value = -0.56 496->497 498 dcoilwtico ≤ -1.12 mae = 0.029 samples = 2 value = -0.721 496->498 499 mae = 0.0 samples = 1 value = -0.692 498->499 500 mae = 0.0 samples = 1 value = -0.751 498->500 502 dcoilwtico ≤ -1.212 mae = 0.044 samples = 2 value = -0.67 501->502 505 mae = 0.0 samples = 1 value = -0.495 501->505 503 mae = 0.0 samples = 1 value = -0.627 502->503 504 mae = 0.0 samples = 1 value = -0.714 502->504 507 mae = 0.0 samples = 1 value = -1.156 506->507 508 dcoilwtico ≤ -1.198 mae = 0.046 samples = 3 value = -0.946 506->508 509 dcoilwtico ≤ -1.212 mae = 0.005 samples = 2 value = -0.941 508->509 512 mae = 0.0 samples = 1 value = -1.073 508->512 510 mae = 0.0 samples = 1 value = -0.946 509->510 511 mae = 0.0 samples = 1 value = -0.936 509->511 514 dcoilwtico ≤ 0.53 mae = 0.036 samples = 3 value = -1.304 513->514 519 mae = 0.0 samples = 1 value = -0.957 513->519 515 day_Tuesday ≤ 0.5 mae = 0.003 samples = 2 value = -1.307 514->515 518 mae = 0.0 samples = 1 value = -1.2 514->518 516 mae = 0.0 samples = 1 value = -1.304 515->516 517 mae = 0.0 samples = 1 value = -1.31 515->517 521 dcoilwtico ≤ 0.501 mae = 0.154 samples = 20 value = -0.7 520->521 560 dcoilwtico ≤ -1.2 mae = 0.128 samples = 5 value = -1.159 520->560 522 day_Tuesday ≤ 0.5 mae = 0.117 samples = 18 value = -0.679 521->522 557 day_Monday ≤ 0.5 mae = 0.045 samples = 2 value = -1.183 521->557 523 dcoilwtico ≤ 0.473 mae = 0.105 samples = 15 value = -0.656 522->523 552 dcoilwtico ≤ -1.43 mae = 0.022 samples = 3 value = -0.819 522->552 524 dcoilwtico ≤ -0.413 mae = 0.099 samples = 14 value = -0.659 523->524 551 mae = 0.0 samples = 1 value = -0.464 523->551 525 dcoilwtico ≤ -1.278 mae = 0.091 samples = 12 value = -0.654 524->525 548 day_Monday ≤ 0.5 mae = 0.051 samples = 2 value = -0.802 524->548 526 day_Monday ≤ 0.5 mae = 0.075 samples = 9 value = -0.661 525->526 543 day_Monday ≤ 0.5 mae = 0.039 samples = 3 value = -0.517 525->543 527 dcoilwtico ≤ -1.463 mae = 0.059 samples = 6 value = -0.654 526->527 538 dcoilwtico ≤ -1.327 mae = 0.07 samples = 3 value = -0.761 526->538 528 mae = 0.0 samples = 1 value = -0.702 527->528 529 dcoilwtico ≤ -1.435 mae = 0.061 samples = 5 value = -0.652 527->529 530 mae = 0.0 samples = 1 value = -0.515 529->530 531 dcoilwtico ≤ -1.312 mae = 0.042 samples = 4 value = -0.654 529->531 532 day_Friday ≤ 0.5 mae = 0.06 samples = 2 value = -0.592 531->532 535 day_Wednesday ≤ 0.5 mae = 0.02 samples = 2 value = -0.677 531->535 533 mae = 0.0 samples = 1 value = -0.532 532->533 534 mae = 0.0 samples = 1 value = -0.652 532->534 536 mae = 0.0 samples = 1 value = -0.656 535->536 537 mae = 0.0 samples = 1 value = -0.697 535->537 539 dcoilwtico ≤ -1.425 mae = 0.05 samples = 2 value = -0.711 538->539 542 mae = 0.0 samples = 1 value = -0.872 538->542 540 mae = 0.0 samples = 1 value = -0.661 539->540 541 mae = 0.0 samples = 1 value = -0.761 539->541 544 mae = 0.0 samples = 1 value = -0.578 543->544 545 transferred_False ≤ 0.5 mae = 0.029 samples = 2 value = -0.488 543->545 546 mae = 0.0 samples = 1 value = -0.459 545->546 547 mae = 0.0 samples = 1 value = -0.517 545->547 549 mae = 0.0 samples = 1 value = -0.752 548->549 550 mae = 0.0 samples = 1 value = -0.853 548->550 553 mae = 0.0 samples = 1 value = -0.877 552->553 554 dcoilwtico ≤ -0.444 mae = 0.004 samples = 2 value = -0.815 552->554 555 mae = 0.0 samples = 1 value = -0.812 554->555 556 mae = 0.0 samples = 1 value = -0.819 554->556 558 mae = 0.0 samples = 1 value = -1.228 557->558 559 mae = 0.0 samples = 1 value = -1.138 557->559 561 dcoilwtico ≤ -1.289 mae = 0.033 samples = 2 value = -0.962 560->561 564 dcoilwtico ≤ -0.354 mae = 0.048 samples = 3 value = -1.264 560->564 562 mae = 0.0 samples = 1 value = -0.995 561->562 563 mae = 0.0 samples = 1 value = -0.93 561->563 565 mae = 0.0 samples = 1 value = -1.159 564->565 566 dcoilwtico ≤ 0.499 mae = 0.019 samples = 2 value = -1.283 564->566 567 mae = 0.0 samples = 1 value = -1.264 566->567 568 mae = 0.0 samples = 1 value = -1.302 566->568 570 month_02 ≤ 0.5 mae = 0.678 samples = 51 value = 1.642 569->570 671 dcoilwtico ≤ -0.41 mae = 0.194 samples = 6 value = 0.255 569->671 571 month_01 ≤ 0.5 mae = 0.577 samples = 47 value = 1.693 570->571 664 dcoilwtico ≤ -1.142 mae = 0.308 samples = 4 value = -0.218 570->664 572 month_05 ≤ 0.5 mae = 0.475 samples = 41 value = 1.767 571->572 653 dcoilwtico ≤ 0.525 mae = 0.401 samples = 6 value = 0.285 571->653 573 dcoilwtico ≤ 0.516 mae = 0.429 samples = 38 value = 1.791 572->573 648 dcoilwtico ≤ -0.875 mae = 0.27 samples = 3 value = 0.846 572->648 574 dcoilwtico ≤ -1.763 mae = 0.341 samples = 35 value = 1.794 573->574 643 month_09 ≤ 0.5 mae = 0.889 samples = 3 value = 0.089 573->643 575 dcoilwtico ≤ -1.857 mae = 0.222 samples = 2 value = 2.568 574->575 578 dcoilwtico ≤ 0.379 mae = 0.315 samples = 33 value = 1.787 574->578 576 mae = 0.0 samples = 1 value = 2.789 575->576 577 mae = 0.0 samples = 1 value = 2.346 575->577 579 dcoilwtico ≤ -1.359 mae = 0.306 samples = 32 value = 1.777 578->579 642 mae = 0.0 samples = 1 value = 2.395 578->642 580 dcoilwtico ≤ -1.656 mae = 0.361 samples = 14 value = 1.868 579->580 607 dcoilwtico ≤ -1.135 mae = 0.227 samples = 18 value = 1.643 579->607 581 month_11 ≤ 0.5 mae = 0.209 samples = 3 value = 1.441 580->581 586 dcoilwtico ≤ -1.382 mae = 0.339 samples = 11 value = 1.928 580->586 582 locale_name_None ≤ 0.5 mae = 0.141 samples = 2 value = 1.301 581->582 585 mae = 0.0 samples = 1 value = 1.787 581->585 583 mae = 0.0 samples = 1 value = 1.441 582->583 584 mae = 0.0 samples = 1 value = 1.16 582->584 587 locale_Local ≤ 0.5 mae = 0.328 samples = 10 value = 1.911 586->587 606 mae = 0.0 samples = 1 value = 2.378 586->606 588 dcoilwtico ≤ -1.461 mae = 0.313 samples = 9 value = 1.894 587->588 605 mae = 0.0 samples = 1 value = 2.354 587->605 589 dcoilwtico ≤ -1.486 mae = 0.249 samples = 5 value = 1.794 588->589 598 month_10 ≤ 0.5 mae = 0.327 samples = 4 value = 1.979 588->598 590 locale_name_None ≤ 0.5 mae = 0.069 samples = 4 value = 1.844 589->590 597 mae = 0.0 samples = 1 value = 0.824 589->597 591 mae = 0.0 samples = 1 value = 1.928 590->591 592 dcoilwtico ≤ -1.629 mae = 0.048 samples = 3 value = 1.794 590->592 593 mae = 0.0 samples = 1 value = 1.894 592->593 594 month_11 ≤ 0.5 mae = 0.022 samples = 2 value = 1.772 592->594 595 mae = 0.0 samples = 1 value = 1.794 594->595 596 mae = 0.0 samples = 1 value = 1.75 594->596 599 dcoilwtico ≤ -1.389 mae = 0.047 samples = 3 value = 1.974 598->599 604 mae = 0.0 samples = 1 value = 3.14 598->604 600 month_09 ≤ 0.5 mae = 0.005 samples = 2 value = 1.979 599->600 603 mae = 0.0 samples = 1 value = 1.842 599->603 601 mae = 0.0 samples = 1 value = 1.974 600->601 602 mae = 0.0 samples = 1 value = 1.984 600->602 608 type_Holiday ≤ 0.5 mae = 0.022 samples = 3 value = 1.374 607->608 613 dcoilwtico ≤ -0.913 mae = 0.212 samples = 15 value = 1.693 607->613 609 month_07 ≤ 0.5 mae = 0.002 samples = 2 value = 1.376 608->609 612 mae = 0.0 samples = 1 value = 1.311 608->612 610 mae = 0.0 samples = 1 value = 1.379 609->610 611 mae = 0.0 samples = 1 value = 1.374 609->611 614 locale_None ≤ 0.5 mae = 0.094 samples = 3 value = 2.098 613->614 619 dcoilwtico ≤ -0.839 mae = 0.16 samples = 12 value = 1.643 613->619 615 mae = 0.0 samples = 1 value = 1.924 614->615 616 month_07 ≤ 0.5 mae = 0.055 samples = 2 value = 2.153 614->616 617 mae = 0.0 samples = 1 value = 2.098 616->617 618 mae = 0.0 samples = 1 value = 2.207 616->618 620 dcoilwtico ≤ -0.855 mae = 0.216 samples = 3 value = 1.693 619->620 625 dcoilwtico ≤ -0.731 mae = 0.134 samples = 9 value = 1.631 619->625 621 dcoilwtico ≤ -0.875 mae = 0.025 samples = 2 value = 1.668 620->621 624 mae = 0.0 samples = 1 value = 2.293 620->624 622 mae = 0.0 samples = 1 value = 1.643 621->622 623 mae = 0.0 samples = 1 value = 1.693 621->623 626 mae = 0.0 samples = 1 value = 1.557 625->626 627 dcoilwtico ≤ -0.348 mae = 0.142 samples = 8 value = 1.637 625->627 628 month_12 ≤ 0.5 mae = 0.062 samples = 2 value = 1.704 627->628 631 month_11 ≤ 0.5 mae = 0.165 samples = 6 value = 1.622 627->631 629 mae = 0.0 samples = 1 value = 1.767 628->629 630 mae = 0.0 samples = 1 value = 1.642 628->630 632 dcoilwtico ≤ 0.085 mae = 0.197 samples = 3 value = 1.631 631->632 637 dcoilwtico ≤ -0.036 mae = 0.1 samples = 3 value = 1.532 631->637 633 mae = 0.0 samples = 1 value = 1.612 632->633 634 dcoilwtico ≤ 0.18 mae = 0.286 samples = 2 value = 1.917 632->634 635 mae = 0.0 samples = 1 value = 2.203 634->635 636 mae = 0.0 samples = 1 value = 1.631 634->636 638 dcoilwtico ≤ -0.113 mae = 0.134 samples = 2 value = 1.666 637->638 641 mae = 0.0 samples = 1 value = 1.499 637->641 639 mae = 0.0 samples = 1 value = 1.532 638->639 640 mae = 0.0 samples = 1 value = 1.8 638->640 644 month_06 ≤ 0.5 mae = 0.053 samples = 2 value = 0.037 643->644 647 mae = 0.0 samples = 1 value = 2.65 643->647 645 mae = 0.0 samples = 1 value = -0.016 644->645 646 mae = 0.0 samples = 1 value = 0.089 644->646 649 mae = 0.0 samples = 1 value = 0.268 648->649 650 type_No_Holiday ≤ 0.5 mae = 0.116 samples = 2 value = 0.962 648->650 651 mae = 0.0 samples = 1 value = 0.846 650->651 652 mae = 0.0 samples = 1 value = 1.079 650->652 654 dcoilwtico ≤ -0.334 mae = 0.399 samples = 5 value = 0.342 653->654 663 mae = 0.0 samples = 1 value = -0.072 653->663 655 dcoilwtico ≤ -1.247 mae = 0.15 samples = 4 value = 0.285 654->655 662 mae = 0.0 samples = 1 value = 1.735 654->662 656 dcoilwtico ≤ -1.336 mae = 0.092 samples = 3 value = 0.342 655->656 661 mae = 0.0 samples = 1 value = 0.018 655->661 657 transferred_None ≤ 0.5 mae = 0.056 samples = 2 value = 0.285 656->657 660 mae = 0.0 samples = 1 value = 0.507 656->660 658 mae = 0.0 samples = 1 value = 0.229 657->658 659 mae = 0.0 samples = 1 value = 0.342 657->659 665 dcoilwtico ≤ -1.205 mae = 0.067 samples = 2 value = 0.102 664->665 668 dcoilwtico ≤ -0.293 mae = 0.042 samples = 2 value = -0.513 664->668 666 mae = 0.0 samples = 1 value = 0.035 665->666 667 mae = 0.0 samples = 1 value = 0.17 665->667 669 mae = 0.0 samples = 1 value = -0.555 668->669 670 mae = 0.0 samples = 1 value = -0.471 668->670 672 dcoilwtico ≤ -1.277 mae = 0.107 samples = 4 value = 0.374 671->672 679 locale_name_Manta ≤ 0.5 mae = 0.106 samples = 2 value = -0.073 671->679 673 dcoilwtico ≤ -1.344 mae = 0.08 samples = 3 value = 0.296 672->673 678 mae = 0.0 samples = 1 value = 0.483 672->678 674 dcoilwtico ≤ -1.432 mae = 0.079 samples = 2 value = 0.374 673->674 677 mae = 0.0 samples = 1 value = 0.215 673->677 675 mae = 0.0 samples = 1 value = 0.296 674->675 676 mae = 0.0 samples = 1 value = 0.453 674->676 680 mae = 0.0 samples = 1 value = -0.179 679->680 681 mae = 0.0 samples = 1 value = 0.033 679->681 683 dcoilwtico ≤ -0.979 mae = 0.218 samples = 27 value = -0.866 682->683 736 dcoilwtico ≤ 0.389 mae = 0.173 samples = 7 value = 0.131 682->736 684 locale_Local ≤ 0.5 mae = 0.168 samples = 15 value = -0.647 683->684 713 dcoilwtico ≤ 0.301 mae = 0.105 samples = 12 value = -1.004 683->713 685 transferred_False ≤ 0.5 mae = 0.145 samples = 13 value = -0.625 684->685 710 locale_name_Riobamba ≤ 0.5 mae = 0.089 samples = 2 value = -0.956 684->710 686 day_Monday ≤ 0.5 mae = 0.105 samples = 11 value = -0.647 685->686 707 dcoilwtico ≤ -1.246 mae = 0.055 samples = 2 value = -0.268 685->707 687 day_Tuesday ≤ 0.5 mae = 0.096 samples = 9 value = -0.625 686->687 704 dcoilwtico ≤ -1.089 mae = 0.021 samples = 2 value = -0.778 686->704 688 dcoilwtico ≤ -1.003 mae = 0.092 samples = 8 value = -0.636 687->688 703 mae = 0.0 samples = 1 value = -0.491 687->703 689 dcoilwtico ≤ -1.093 mae = 0.109 samples = 5 value = -0.601 688->689 698 day_Wednesday ≤ 0.5 mae = 0.031 samples = 3 value = -0.655 688->698 690 dcoilwtico ≤ -1.254 mae = 0.103 samples = 4 value = -0.613 689->690 697 mae = 0.0 samples = 1 value = -0.469 689->697 691 mae = 0.0 samples = 1 value = -0.597 690->691 692 day_Thursday ≤ 0.5 mae = 0.128 samples = 3 value = -0.625 690->692 693 dcoilwtico ≤ -1.202 mae = 0.012 samples = 2 value = -0.613 692->693 696 mae = 0.0 samples = 1 value = -0.984 692->696 694 mae = 0.0 samples = 1 value = -0.625 693->694 695 mae = 0.0 samples = 1 value = -0.601 693->695 699 mae = 0.0 samples = 1 value = -0.647 698->699 700 dcoilwtico ≤ -0.987 mae = 0.042 samples = 2 value = -0.697 698->700 701 mae = 0.0 samples = 1 value = -0.739 700->701 702 mae = 0.0 samples = 1 value = -0.655 700->702 705 mae = 0.0 samples = 1 value = -0.757 704->705 706 mae = 0.0 samples = 1 value = -0.799 704->706 708 mae = 0.0 samples = 1 value = -0.213 707->708 709 mae = 0.0 samples = 1 value = -0.323 707->709 711 mae = 0.0 samples = 1 value = -1.045 710->711 712 mae = 0.0 samples = 1 value = -0.866 710->712 714 day_Wednesday ≤ 0.5 mae = 0.031 samples = 4 value = -0.952 713->714 721 day_Wednesday ≤ 0.5 mae = 0.109 samples = 8 value = -1.089 713->721 715 dcoilwtico ≤ -0.97 mae = 0.018 samples = 3 value = -0.967 714->715 720 mae = 0.0 samples = 1 value = -0.9 714->720 716 mae = 0.0 samples = 1 value = -0.938 715->716 717 day_Tuesday ≤ 0.5 mae = 0.013 samples = 2 value = -0.98 715->717 718 mae = 0.0 samples = 1 value = -0.967 717->718 719 mae = 0.0 samples = 1 value = -0.993 717->719 722 dcoilwtico ≤ 0.345 mae = 0.081 samples = 7 value = -1.1 721->722 735 mae = 0.0 samples = 1 value = -0.797 721->735 723 mae = 0.0 samples = 1 value = -1.322 722->723 724 dcoilwtico ≤ 0.508 mae = 0.058 samples = 6 value = -1.089 722->724 725 day_Tuesday ≤ 0.5 mae = 0.052 samples = 5 value = -1.078 724->725 734 mae = 0.0 samples = 1 value = -1.165 724->734 726 dcoilwtico ≤ 0.365 mae = 0.018 samples = 3 value = -1.1 725->726 731 dcoilwtico ≤ 0.373 mae = 0.028 samples = 2 value = -0.987 725->731 727 mae = 0.0 samples = 1 value = -1.131 726->727 728 locale_Local ≤ 0.5 mae = 0.011 samples = 2 value = -1.089 726->728 729 mae = 0.0 samples = 1 value = -1.1 728->729 730 mae = 0.0 samples = 1 value = -1.078 728->730 732 mae = 0.0 samples = 1 value = -1.015 731->732 733 mae = 0.0 samples = 1 value = -0.958 731->733 737 dcoilwtico ≤ -0.329 mae = 0.073 samples = 4 value = 0.185 736->737 744 dcoilwtico ≤ 0.542 mae = 0.136 samples = 3 value = -0.271 736->744 738 dcoilwtico ≤ -1.088 mae = 0.025 samples = 3 value = 0.186 737->738 743 mae = 0.0 samples = 1 value = -0.031 737->743 739 dcoilwtico ≤ -1.197 mae = 0.001 samples = 2 value = 0.185 738->739 742 mae = 0.0 samples = 1 value = 0.261 738->742 740 mae = 0.0 samples = 1 value = 0.184 739->740 741 mae = 0.0 samples = 1 value = 0.186 739->741 745 dcoilwtico ≤ 0.486 mae = 0.201 samples = 2 value = -0.07 744->745 748 mae = 0.0 samples = 1 value = -0.278 744->748 746 mae = 0.0 samples = 1 value = -0.271 745->746 747 mae = 0.0 samples = 1 value = 0.131 745->747 750 month_08 ≤ 0.5 mae = 0.569 samples = 15 value = 2.329 749->750 779 month_10 ≤ 0.5 mae = 0.763 samples = 51 value = 1.049 749->779 751 dcoilwtico ≤ -1.41 mae = 0.413 samples = 11 value = 2.403 750->751 772 dcoilwtico ≤ -1.444 mae = 0.509 samples = 4 value = 1.538 750->772 752 dcoilwtico ≤ -1.854 mae = 0.353 samples = 10 value = 2.389 751->752 771 mae = 0.0 samples = 1 value = 3.408 751->771 753 mae = 0.0 samples = 1 value = 2.973 752->753 754 locale_name_None ≤ 0.5 mae = 0.326 samples = 9 value = 2.375 752->754 755 mae = 0.0 samples = 1 value = 2.759 754->755 756 month_10 ≤ 0.5 mae = 0.319 samples = 8 value = 2.352 754->756 757 dcoilwtico ≤ -1.412 mae = 0.313 samples = 7 value = 2.375 756->757 770 mae = 0.0 samples = 1 value = 2.014 756->770 758 month_09 ≤ 0.5 mae = 0.341 samples = 6 value = 2.389 757->758 769 mae = 0.0 samples = 1 value = 2.231 757->769 759 dcoilwtico ≤ -1.647 mae = 0.346 samples = 5 value = 2.375 758->759 768 mae = 0.0 samples = 1 value = 2.69 758->768 760 dcoilwtico ≤ -1.811 mae = 0.514 samples = 3 value = 2.329 759->760 765 dcoilwtico ≤ -1.549 mae = 0.044 samples = 2 value = 2.447 759->765 761 mae = 0.0 samples = 1 value = 2.375 760->761 762 month_12 ≤ 0.5 mae = 0.748 samples = 2 value = 1.581 760->762 763 mae = 0.0 samples = 1 value = 2.329 762->763 764 mae = 0.0 samples = 1 value = 0.833 762->764 766 mae = 0.0 samples = 1 value = 2.491 765->766 767 mae = 0.0 samples = 1 value = 2.403 765->767 773 dcoilwtico ≤ -1.522 mae = 0.45 samples = 3 value = 1.316 772->773 778 mae = 0.0 samples = 1 value = 1.999 772->778 774 dcoilwtico ≤ -1.638 mae = 0.222 samples = 2 value = 1.538 773->774 777 mae = 0.0 samples = 1 value = 0.408 773->777 775 mae = 0.0 samples = 1 value = 1.316 774->775 776 mae = 0.0 samples = 1 value = 1.759 774->776 780 month_09 ≤ 0.5 mae = 0.699 samples = 46 value = 0.986 779->780 871 dcoilwtico ≤ 0.331 mae = 0.413 samples = 5 value = 2.391 779->871 781 month_04 ≤ 0.5 mae = 0.651 samples = 44 value = 0.912 780->781 868 dcoilwtico ≤ 0.508 mae = 0.05 samples = 2 value = 2.732 780->868 782 month_03 ≤ 0.5 mae = 0.659 samples = 38 value = 1.031 781->782 857 dcoilwtico ≤ -0.317 mae = 0.129 samples = 6 value = 0.395 781->857 783 dcoilwtico ≤ -1.05 mae = 0.661 samples = 32 value = 1.14 782->783 846 dcoilwtico ≤ 0.465 mae = 0.259 samples = 6 value = 0.444 782->846 784 month_08 ≤ 0.5 mae = 0.414 samples = 12 value = 0.888 783->784 807 dcoilwtico ≤ 0.513 mae = 0.679 samples = 20 value = 1.849 783->807 785 month_07 ≤ 0.5 mae = 0.323 samples = 11 value = 0.784 784->785 806 mae = 0.0 samples = 1 value = 2.199 784->806 786 dcoilwtico ≤ -1.173 mae = 0.204 samples = 7 value = 0.699 785->786 799 dcoilwtico ≤ -1.121 mae = 0.3 samples = 4 value = 1.275 785->799 787 month_11 ≤ 0.5 mae = 0.172 samples = 6 value = 0.741 786->787 798 mae = 0.0 samples = 1 value = 0.303 786->798 788 dcoilwtico ≤ -1.36 mae = 0.148 samples = 5 value = 0.699 787->788 797 mae = 0.0 samples = 1 value = 0.991 787->797 789 mae = 0.0 samples = 1 value = 0.784 788->789 790 dcoilwtico ≤ -1.32 mae = 0.163 samples = 4 value = 0.651 788->790 791 mae = 0.0 samples = 1 value = 0.604 790->791 792 dcoilwtico ≤ -1.277 mae = 0.186 samples = 3 value = 0.699 790->792 793 mae = 0.0 samples = 1 value = 1.073 792->793 794 month_01 ≤ 0.5 mae = 0.092 samples = 2 value = 0.607 792->794 795 mae = 0.0 samples = 1 value = 0.515 794->795 796 mae = 0.0 samples = 1 value = 0.699 794->796 800 dcoilwtico ≤ -1.291 mae = 0.144 samples = 3 value = 1.341 799->800 805 mae = 0.0 samples = 1 value = 0.573 799->805 801 mae = 0.0 samples = 1 value = 1.208 800->801 802 dcoilwtico ≤ -1.188 mae = 0.149 samples = 2 value = 1.49 800->802 803 mae = 0.0 samples = 1 value = 1.639 802->803 804 mae = 0.0 samples = 1 value = 1.341 802->804 808 month_05 ≤ 0.5 mae = 0.475 samples = 15 value = 1.953 807->808 837 dcoilwtico ≤ 0.553 mae = 0.655 samples = 5 value = 0.414 807->837 809 type_Holiday ≤ 0.5 mae = 0.357 samples = 12 value = 2.081 808->809 832 dcoilwtico ≤ -0.835 mae = 0.294 samples = 3 value = 0.981 808->832 810 month_06 ≤ 0.5 mae = 0.288 samples = 11 value = 2.125 809->810 831 mae = 0.0 samples = 1 value = 1.012 809->831 811 dcoilwtico ≤ -0.338 mae = 0.198 samples = 7 value = 2.173 810->811 824 dcoilwtico ≤ -0.839 mae = 0.344 samples = 4 value = 1.907 810->824 812 month_11 ≤ 0.5 mae = 0.152 samples = 3 value = 2.481 811->812 817 dcoilwtico ≤ 0.2 mae = 0.108 samples = 4 value = 2.084 811->817 813 locale_name_None ≤ 0.5 mae = 0.127 samples = 2 value = 2.354 812->813 816 mae = 0.0 samples = 1 value = 2.684 812->816 814 mae = 0.0 samples = 1 value = 2.481 813->814 815 mae = 0.0 samples = 1 value = 2.228 813->815 818 dcoilwtico ≤ -0.171 mae = 0.046 samples = 3 value = 2.131 817->818 823 mae = 0.0 samples = 1 value = 1.836 817->823 819 mae = 0.0 samples = 1 value = 2.037 818->819 820 dcoilwtico ≤ -0.127 mae = 0.021 samples = 2 value = 2.152 818->820 821 mae = 0.0 samples = 1 value = 2.131 820->821 822 mae = 0.0 samples = 1 value = 2.173 820->822 825 dcoilwtico ≤ -0.889 mae = 0.088 samples = 3 value = 1.953 824->825 830 mae = 0.0 samples = 1 value = 0.843 824->830 826 mae = 0.0 samples = 1 value = 1.861 825->826 827 dcoilwtico ≤ -0.864 mae = 0.086 samples = 2 value = 2.039 825->827 828 mae = 0.0 samples = 1 value = 2.125 827->828 829 mae = 0.0 samples = 1 value = 1.953 827->829 833 transferred_None ≤ 0.5 mae = 0.163 samples = 2 value = 0.818 832->833 836 mae = 0.0 samples = 1 value = 1.539 832->836 834 mae = 0.0 samples = 1 value = 0.655 833->834 835 mae = 0.0 samples = 1 value = 0.981 833->835 838 month_12 ≤ 0.5 mae = 0.261 samples = 4 value = 0.346 837->838 845 mae = 0.0 samples = 1 value = 2.648 837->845 839 dcoilwtico ≤ 0.55 mae = 0.091 samples = 3 value = 0.277 838->839 844 mae = 0.0 samples = 1 value = 1.049 838->844 840 dcoilwtico ≤ 0.543 mae = 0.068 samples = 2 value = 0.21 839->840 843 mae = 0.0 samples = 1 value = 0.414 839->843 841 mae = 0.0 samples = 1 value = 0.142 840->841 842 mae = 0.0 samples = 1 value = 0.277 840->842 847 dcoilwtico ≤ -1.336 mae = 0.191 samples = 5 value = 0.453 846->847 856 mae = 0.0 samples = 1 value = -0.147 846->856 848 mae = 0.0 samples = 1 value = 0.509 847->848 849 dcoilwtico ≤ -1.279 mae = 0.225 samples = 4 value = 0.444 847->849 850 mae = 0.0 samples = 1 value = 0.316 849->850 851 dcoilwtico ≤ -1.255 mae = 0.254 samples = 3 value = 0.453 849->851 852 mae = 0.0 samples = 1 value = 1.196 851->852 853 dcoilwtico ≤ -0.411 mae = 0.009 samples = 2 value = 0.444 851->853 854 mae = 0.0 samples = 1 value = 0.453 853->854 855 mae = 0.0 samples = 1 value = 0.435 853->855 858 dcoilwtico ≤ -0.999 mae = 0.092 samples = 4 value = 0.442 857->858 865 transferred_False ≤ 0.5 mae = 0.063 samples = 2 value = 0.198 857->865 859 type_Holiday ≤ 0.5 mae = 0.031 samples = 3 value = 0.401 858->859 864 mae = 0.0 samples = 1 value = 0.677 858->864 860 dcoilwtico ≤ -1.099 mae = 0.006 samples = 2 value = 0.395 859->860 863 mae = 0.0 samples = 1 value = 0.482 859->863 861 mae = 0.0 samples = 1 value = 0.401 860->861 862 mae = 0.0 samples = 1 value = 0.389 860->862 866 mae = 0.0 samples = 1 value = 0.261 865->866 867 mae = 0.0 samples = 1 value = 0.135 865->867 869 mae = 0.0 samples = 1 value = 2.682 868->869 870 mae = 0.0 samples = 1 value = 2.781 868->870 872 dcoilwtico ≤ -1.363 mae = 0.284 samples = 4 value = 2.187 871->872 879 mae = 0.0 samples = 1 value = 3.322 871->879 873 mae = 0.0 samples = 1 value = 2.391 872->873 874 dcoilwtico ≤ -0.607 mae = 0.242 samples = 3 value = 1.983 872->874 875 mae = 0.0 samples = 1 value = 1.691 874->875 876 dcoilwtico ≤ 0.178 mae = 0.217 samples = 2 value = 2.2 874->876 877 mae = 0.0 samples = 1 value = 2.417 876->877 878 mae = 0.0 samples = 1 value = 1.983 876->878 881 day_Saturday ≤ 0.5 mae = 0.51 samples = 366 value = -0.738 880->881 1612 locale_name_Ibarra ≤ 0.5 mae = 0.602 samples = 65 value = 0.122 880->1612 882 month_12 ≤ 0.5 mae = 0.415 samples = 304 value = -0.843 881->882 1489 dcoilwtico ≤ 0.562 mae = 0.461 samples = 62 value = 0.033 881->1489 883 month_03 ≤ 0.5 mae = 0.408 samples = 287 value = -0.885 882->883 1456 day_Monday ≤ 0.5 mae = 0.332 samples = 17 value = -0.425 882->1456 884 day_Thursday ≤ 0.5 mae = 0.382 samples = 267 value = -0.911 883->884 1417 dcoilwtico ≤ 0.739 mae = 0.582 samples = 20 value = -0.236 883->1417 885 day_Tuesday ≤ 0.5 mae = 0.371 samples = 214 value = -0.843 884->885 1312 dcoilwtico ≤ 0.621 mae = 0.367 samples = 53 value = -1.112 884->1312 886 locale_name_Cayambe ≤ 0.5 mae = 0.374 samples = 163 value = -0.815 885->886 1211 type_Event ≤ 0.5 mae = 0.319 samples = 51 value = -0.999 885->1211 887 locale_name_Guaranda ≤ 0.5 mae = 0.367 samples = 162 value = -0.818 886->887 1210 mae = 0.0 samples = 1 value = 0.763 886->1210 888 dcoilwtico ≤ 1.055 mae = 0.363 samples = 161 value = -0.822 887->888 1209 mae = 0.0 samples = 1 value = 0.193 887->1209 889 month_07 ≤ 0.5 mae = 0.398 samples = 130 value = -0.788 888->889 1148 month_06 ≤ 0.5 mae = 0.179 samples = 31 value = -0.952 888->1148 890 month_08 ≤ 0.5 mae = 0.319 samples = 115 value = -0.822 889->890 1119 locale_Local ≤ 0.5 mae = 0.624 samples = 15 value = 0.247 889->1119 891 type_Transfer ≤ 0.5 mae = 0.324 samples = 106 value = -0.828 890->891 1102 dcoilwtico ≤ 0.877 mae = 0.154 samples = 9 value = -0.492 890->1102 892 month_01 ≤ 0.5 mae = 0.319 samples = 105 value = -0.825 891->892 1101 mae = 0.0 samples = 1 value = -1.674 891->1101 893 month_04 ≤ 0.5 mae = 0.243 samples = 90 value = -0.828 892->893 1072 dcoilwtico ≤ 0.672 mae = 0.719 samples = 15 value = -0.038 892->1072 894 dcoilwtico ≤ 0.57 mae = 0.253 samples = 78 value = -0.836 893->894 1049 dcoilwtico ≤ 0.605 mae = 0.137 samples = 12 value = -0.724 893->1049 895 day_Monday ≤ 0.5 mae = 0.893 samples = 2 value = 0.275 894->895 898 month_10 ≤ 0.5 mae = 0.231 samples = 76 value = -0.838 894->898 896 mae = 0.0 samples = 1 value = 1.169 895->896 897 mae = 0.0 samples = 1 value = -0.618 895->897 899 dcoilwtico ≤ 1.04 mae = 0.241 samples = 67 value = -0.835 898->899 1032 dcoilwtico ≤ 0.981 mae = 0.106 samples = 9 value = -0.95 898->1032 900 locale_name_Ecuador ≤ 0.5 mae = 0.245 samples = 65 value = -0.835 899->900 1029 dcoilwtico ≤ 1.042 mae = 0.004 samples = 2 value = -0.726 899->1029 901 month_06 ≤ 0.5 mae = 0.246 samples = 64 value = -0.836 900->901 1028 mae = 0.0 samples = 1 value = -0.633 900->1028 902 day_Friday ≤ 0.5 mae = 0.254 samples = 53 value = -0.831 901->902 1007 dcoilwtico ≤ 0.859 mae = 0.19 samples = 11 value = -0.962 901->1007 903 locale_name_Santo Domingo de los Tsachilas ≤ 0.5 mae = 0.288 samples = 36 value = -0.836 902->903 974 month_09 ≤ 0.5 mae = 0.174 samples = 17 value = -0.793 902->974 904 dcoilwtico ≤ 0.716 mae = 0.292 samples = 35 value = -0.836 903->904 973 mae = 0.0 samples = 1 value = -0.706 903->973 905 month_09 ≤ 0.5 mae = 0.467 samples = 18 value = -0.893 904->905 940 dcoilwtico ≤ 0.828 mae = 0.101 samples = 17 value = -0.835 904->940 906 month_11 ≤ 0.5 mae = 0.11 samples = 15 value = -0.936 905->906 935 dcoilwtico ≤ 0.596 mae = 0.435 samples = 3 value = 1.749 905->935 907 dcoilwtico ≤ 0.581 mae = 0.104 samples = 12 value = -0.986 906->907 930 day_Wednesday ≤ 0.5 mae = 0.007 samples = 3 value = -0.831 906->930 908 mae = 0.0 samples = 1 value = -0.873 907->908 909 dcoilwtico ≤ 0.675 mae = 0.103 samples = 11 value = -0.987 907->909 910 day_Monday ≤ 0.5 mae = 0.062 samples = 5 value = -1.055 909->910 919 dcoilwtico ≤ 0.676 mae = 0.11 samples = 6 value = -0.924 909->919 911 mae = 0.0 samples = 1 value = -0.985 910->911 912 dcoilwtico ≤ 0.607 mae = 0.06 samples = 4 value = -1.073 910->912 913 mae = 0.0 samples = 1 value = -1.19 912->913 914 dcoilwtico ≤ 0.665 mae = 0.034 samples = 3 value = -1.055 912->914 915 dcoilwtico ≤ 0.635 mae = 0.034 samples = 2 value = -1.021 914->915 918 mae = 0.0 samples = 1 value = -1.09 914->918 916 mae = 0.0 samples = 1 value = -1.055 915->916 917 mae = 0.0 samples = 1 value = -0.987 915->917 920 mae = 0.0 samples = 1 value = -0.769 919->920 921 dcoilwtico ≤ 0.697 mae = 0.099 samples = 5 value = -0.936 919->921 922 month_05 ≤ 0.5 mae = 0.091 samples = 4 value = -0.924 921->922 929 mae = 0.0 samples = 1 value = -1.066 921->929 923 dcoilwtico ≤ 0.685 mae = 0.087 samples = 3 value = -0.912 922->923 928 mae = 0.0 samples = 1 value = -1.015 922->928 924 day_Wednesday ≤ 0.5 mae = 0.118 samples = 2 value = -0.794 923->924 927 mae = 0.0 samples = 1 value = -0.936 923->927 925 mae = 0.0 samples = 1 value = -0.675 924->925 926 mae = 0.0 samples = 1 value = -0.912 924->926 931 transferred_False ≤ 0.5 mae = 0.003 samples = 2 value = -0.828 930->931 934 mae = 0.0 samples = 1 value = -0.845 930->934 932 mae = 0.0 samples = 1 value = -0.825 931->932 933 mae = 0.0 samples = 1 value = -0.831 931->933 936 mae = 0.0 samples = 1 value = 1.776 935->936 937 day_Wednesday ≤ 0.5 mae = 0.639 samples = 2 value = 1.11 935->937 938 mae = 0.0 samples = 1 value = 0.47 937->938 939 mae = 0.0 samples = 1 value = 1.749 937->939 941 day_Monday ≤ 0.5 mae = 0.022 samples = 2 value = -0.637 940->941 944 month_09 ≤ 0.5 mae = 0.089 samples = 15 value = -0.835 940->944 942 mae = 0.0 samples = 1 value = -0.615 941->942 943 mae = 0.0 samples = 1 value = -0.66 941->943 945 dcoilwtico ≤ 0.947 mae = 0.071 samples = 12 value = -0.829 944->945 968 dcoilwtico ≤ 0.936 mae = 0.067 samples = 3 value = -0.973 944->968 946 dcoilwtico ≤ 0.872 mae = 0.084 samples = 5 value = -0.755 945->946 955 dcoilwtico ≤ 0.953 mae = 0.05 samples = 7 value = -0.835 945->955 947 locale_name_Puyo ≤ 0.5 mae = 0.017 samples = 2 value = -0.852 946->947 950 dcoilwtico ≤ 0.895 mae = 0.069 samples = 3 value = -0.737 946->950 948 mae = 0.0 samples = 1 value = -0.869 947->948 949 mae = 0.0 samples = 1 value = -0.835 947->949 951 mae = 0.0 samples = 1 value = -0.547 950->951 952 day_Wednesday ≤ 0.5 mae = 0.009 samples = 2 value = -0.746 950->952 953 mae = 0.0 samples = 1 value = -0.737 952->953 954 mae = 0.0 samples = 1 value = -0.755 952->954 956 mae = 0.0 samples = 1 value = -0.991 955->956 957 dcoilwtico ≤ 0.958 mae = 0.032 samples = 6 value = -0.829 955->957 958 mae = 0.0 samples = 1 value = -0.813 957->958 959 dcoilwtico ≤ 0.967 mae = 0.034 samples = 5 value = -0.835 957->959 960 mae = 0.0 samples = 1 value = -0.855 959->960 961 month_02 ≤ 0.5 mae = 0.037 samples = 4 value = -0.829 959->961 962 dcoilwtico ≤ 0.991 mae = 0.005 samples = 3 value = -0.835 961->962 967 mae = 0.0 samples = 1 value = -0.7 961->967 963 mae = 0.0 samples = 1 value = -0.836 962->963 964 dcoilwtico ≤ 1.021 mae = 0.007 samples = 2 value = -0.829 962->964 965 mae = 0.0 samples = 1 value = -0.822 964->965 966 mae = 0.0 samples = 1 value = -0.835 964->966 969 mae = 0.0 samples = 1 value = -1.107 968->969 970 dcoilwtico ≤ 0.962 mae = 0.033 samples = 2 value = -0.94 968->970 971 mae = 0.0 samples = 1 value = -0.907 970->971 972 mae = 0.0 samples = 1 value = -0.973 970->972 975 dcoilwtico ≤ 0.695 mae = 0.101 samples = 14 value = -0.79 974->975 1002 dcoilwtico ≤ 0.798 mae = 0.46 samples = 3 value = -0.96 974->1002 976 dcoilwtico ≤ 0.631 mae = 0.068 samples = 5 value = -0.822 975->976 985 month_05 ≤ 0.5 mae = 0.1 samples = 9 value = -0.737 975->985 977 dcoilwtico ≤ 0.605 mae = 0.012 samples = 3 value = -0.793 976->977 982 month_05 ≤ 0.5 mae = 0.03 samples = 2 value = -0.96 976->982 978 dcoilwtico ≤ 0.589 mae = 0.003 samples = 2 value = -0.79 977->978 981 mae = 0.0 samples = 1 value = -0.822 977->981 979 mae = 0.0 samples = 1 value = -0.787 978->979 980 mae = 0.0 samples = 1 value = -0.793 978->980 983 mae = 0.0 samples = 1 value = -0.99 982->983 984 mae = 0.0 samples = 1 value = -0.93 982->984 986 dcoilwtico ≤ 0.837 mae = 0.095 samples = 5 value = -0.75 985->986 995 dcoilwtico ≤ 0.84 mae = 0.073 samples = 4 value = -0.67 985->995 987 dcoilwtico ≤ 0.778 mae = 0.006 samples = 2 value = -0.744 986->987 990 dcoilwtico ≤ 0.944 mae = 0.092 samples = 3 value = -0.935 986->990 988 mae = 0.0 samples = 1 value = -0.75 987->988 989 mae = 0.0 samples = 1 value = -0.737 987->989 991 dcoilwtico ≤ 0.89 mae = 0.036 samples = 2 value = -0.971 990->991 994 mae = 0.0 samples = 1 value = -0.733 990->994 992 mae = 0.0 samples = 1 value = -1.008 991->992 993 mae = 0.0 samples = 1 value = -0.935 991->993 996 mae = 0.0 samples = 1 value = -0.516 995->996 997 dcoilwtico ≤ 0.983 mae = 0.044 samples = 3 value = -0.677 995->997 998 dcoilwtico ≤ 0.886 mae = 0.007 samples = 2 value = -0.67 997->998 1001 mae = 0.0 samples = 1 value = -0.794 997->1001 999 mae = 0.0 samples = 1 value = -0.663 998->999 1000 mae = 0.0 samples = 1 value = -0.677 998->1000 1003 mae = 0.0 samples = 1 value = 0.266 1002->1003 1004 dcoilwtico ≤ 0.988 mae = 0.077 samples = 2 value = -1.036 1002->1004 1005 mae = 0.0 samples = 1 value = -1.113 1004->1005 1006 mae = 0.0 samples = 1 value = -0.96 1004->1006 1008 dcoilwtico ≤ 0.664 mae = 0.08 samples = 8 value = -0.997 1007->1008 1023 day_Monday ≤ 0.5 mae = 0.048 samples = 3 value = -0.491 1007->1023 1009 dcoilwtico ≤ 0.642 mae = 0.022 samples = 3 value = -1.07 1008->1009 1014 dcoilwtico ≤ 0.675 mae = 0.061 samples = 5 value = -0.962 1008->1014 1010 day_Friday ≤ 0.5 mae = 0.021 samples = 2 value = -1.091 1009->1010 1013 mae = 0.0 samples = 1 value = -1.046 1009->1013 1011 mae = 0.0 samples = 1 value = -1.112 1010->1011 1012 mae = 0.0 samples = 1 value = -1.07 1010->1012 1015 mae = 0.0 samples = 1 value = -0.84 1014->1015 1016 day_Monday ≤ 0.5 mae = 0.046 samples = 4 value = -0.965 1014->1016 1017 dcoilwtico ≤ 0.711 mae = 0.022 samples = 3 value = -0.968 1016->1017 1022 mae = 0.0 samples = 1 value = -0.848 1016->1022 1018 mae = 0.0 samples = 1 value = -1.027 1017->1018 1019 day_Friday ≤ 0.5 mae = 0.003 samples = 2 value = -0.965 1017->1019 1020 mae = 0.0 samples = 1 value = -0.968 1019->1020 1021 mae = 0.0 samples = 1 value = -0.962 1019->1021 1024 day_Wednesday ≤ 0.5 mae = 0.03 samples = 2 value = -0.521 1023->1024 1027 mae = 0.0 samples = 1 value = -0.408 1023->1027 1025 mae = 0.0 samples = 1 value = -0.551 1024->1025 1026 mae = 0.0 samples = 1 value = -0.491 1024->1026 1030 mae = 0.0 samples = 1 value = -0.722 1029->1030 1031 mae = 0.0 samples = 1 value = -0.73 1029->1031 1033 day_Monday ≤ 0.5 mae = 0.075 samples = 8 value = -0.95 1032->1033 1048 mae = 0.0 samples = 1 value = -0.602 1032->1048 1034 dcoilwtico ≤ 0.914 mae = 0.057 samples = 5 value = -0.95 1033->1034 1043 transferred_None ≤ 0.5 mae = 0.06 samples = 3 value = -1.086 1033->1043 1035 dcoilwtico ≤ 0.795 mae = 0.038 samples = 4 value = -0.95 1034->1035 1042 mae = 0.0 samples = 1 value = -0.815 1034->1042 1036 dcoilwtico ≤ 0.713 mae = 0.042 samples = 2 value = -0.993 1035->1036 1039 locale_National ≤ 0.5 mae = 0.033 samples = 2 value = -0.917 1035->1039 1037 mae = 0.0 samples = 1 value = -0.951 1036->1037 1038 mae = 0.0 samples = 1 value = -1.034 1036->1038 1040 mae = 0.0 samples = 1 value = -0.884 1039->1040 1041 mae = 0.0 samples = 1 value = -0.95 1039->1041 1044 mae = 0.0 samples = 1 value = -0.945 1043->1044 1045 dcoilwtico ≤ 0.868 mae = 0.02 samples = 2 value = -1.105 1043->1045 1046 mae = 0.0 samples = 1 value = -1.086 1045->1046 1047 mae = 0.0 samples = 1 value = -1.125 1045->1047 1050 locale_name_Ecuador ≤ 0.5 mae = 0.12 samples = 2 value = -1.108 1049->1050 1053 locale_National ≤ 0.5 mae = 0.087 samples = 10 value = -0.716 1049->1053 1051 mae = 0.0 samples = 1 value = -0.988 1050->1051 1052 mae = 0.0 samples = 1 value = -1.228 1050->1052 1054 locale_Regional ≤ 0.5 mae = 0.066 samples = 9 value = -0.721 1053->1054 1071 mae = 0.0 samples = 1 value = -0.444 1053->1071 1055 locale_name_Libertad ≤ 0.5 mae = 0.063 samples = 8 value = -0.724 1054->1055 1070 mae = 0.0 samples = 1 value = -0.637 1054->1070 1056 day_Monday ≤ 0.5 mae = 0.063 samples = 7 value = -0.721 1055->1056 1069 mae = 0.0 samples = 1 value = -0.789 1055->1069 1057 dcoilwtico ≤ 0.746 mae = 0.073 samples = 5 value = -0.728 1056->1057 1066 locale_name_Riobamba ≤ 0.5 mae = 0.024 samples = 2 value = -0.688 1056->1066 1058 mae = 0.0 samples = 1 value = -0.721 1057->1058 1059 dcoilwtico ≤ 0.873 mae = 0.09 samples = 4 value = -0.73 1057->1059 1060 mae = 0.0 samples = 1 value = -0.914 1059->1060 1061 dcoilwtico ≤ 0.886 mae = 0.058 samples = 3 value = -0.728 1059->1061 1062 mae = 0.0 samples = 1 value = -0.56 1061->1062 1063 day_Friday ≤ 0.5 mae = 0.002 samples = 2 value = -0.73 1061->1063 1064 mae = 0.0 samples = 1 value = -0.728 1063->1064 1065 mae = 0.0 samples = 1 value = -0.733 1063->1065 1067 mae = 0.0 samples = 1 value = -0.664 1066->1067 1068 mae = 0.0 samples = 1 value = -0.711 1066->1068 1073 dcoilwtico ≤ 0.612 mae = 0.671 samples = 10 value = -0.959 1072->1073 1092 dcoilwtico ≤ 0.739 mae = 0.367 samples = 5 value = 0.141 1072->1092 1074 dcoilwtico ≤ 0.567 mae = 0.619 samples = 4 value = 0.552 1073->1074 1081 day_Friday ≤ 0.5 mae = 0.236 samples = 6 value = -1.064 1073->1081 1075 mae = 0.0 samples = 1 value = -0.94 1074->1075 1076 day_Wednesday ≤ 0.5 mae = 0.257 samples = 3 value = 0.764 1074->1076 1077 day_Monday ≤ 0.5 mae = 0.174 samples = 2 value = 0.938 1076->1077 1080 mae = 0.0 samples = 1 value = 0.34 1076->1080 1078 mae = 0.0 samples = 1 value = 0.764 1077->1078 1079 mae = 0.0 samples = 1 value = 1.111 1077->1079 1082 dcoilwtico ≤ 0.642 mae = 0.307 samples = 4 value = -1.099 1081->1082 1089 dcoilwtico ≤ 0.639 mae = 0.026 samples = 2 value = -1.004 1081->1089 1083 mae = 0.0 samples = 1 value = -1.102 1082->1083 1084 dcoilwtico ≤ 0.66 mae = 0.408 samples = 3 value = -1.097 1082->1084 1085 mae = 0.0 samples = 1 value = -0.038 1084->1085 1086 dcoilwtico ≤ 0.664 mae = 0.082 samples = 2 value = -1.18 1084->1086 1087 mae = 0.0 samples = 1 value = -1.262 1086->1087 1088 mae = 0.0 samples = 1 value = -1.097 1086->1088 1090 mae = 0.0 samples = 1 value = -1.03 1089->1090 1091 mae = 0.0 samples = 1 value = -0.978 1089->1091 1093 dcoilwtico ≤ 0.686 mae = 0.142 samples = 4 value = 0.199 1092->1093 1100 mae = 0.0 samples = 1 value = -1.127 1092->1100 1094 mae = 0.0 samples = 1 value = 0.547 1093->1094 1095 day_Friday ≤ 0.5 mae = 0.053 samples = 3 value = 0.141 1093->1095 1096 mae = 0.0 samples = 1 value = 0.256 1095->1096 1097 dcoilwtico ≤ 0.711 mae = 0.022 samples = 2 value = 0.118 1095->1097 1098 mae = 0.0 samples = 1 value = 0.096 1097->1098 1099 mae = 0.0 samples = 1 value = 0.141 1097->1099 1103 dcoilwtico ≤ 0.67 mae = 0.069 samples = 7 value = -0.487 1102->1103 1116 dcoilwtico ≤ 1.005 mae = 0.076 samples = 2 value = -0.939 1102->1116 1104 mae = 0.0 samples = 1 value = -0.46 1103->1104 1105 dcoilwtico ≤ 0.737 mae = 0.076 samples = 6 value = -0.49 1103->1105 1106 dcoilwtico ≤ 0.694 mae = 0.089 samples = 4 value = -0.566 1105->1106 1113 day_Friday ≤ 0.5 mae = 0.046 samples = 2 value = -0.441 1105->1113 1107 dcoilwtico ≤ 0.683 mae = 0.022 samples = 2 value = -0.47 1106->1107 1110 day_Wednesday ≤ 0.5 mae = 0.007 samples = 2 value = -0.647 1106->1110 1108 mae = 0.0 samples = 1 value = -0.492 1107->1108 1109 mae = 0.0 samples = 1 value = -0.447 1107->1109 1111 mae = 0.0 samples = 1 value = -0.654 1110->1111 1112 mae = 0.0 samples = 1 value = -0.64 1110->1112 1114 mae = 0.0 samples = 1 value = -0.487 1113->1114 1115 mae = 0.0 samples = 1 value = -0.396 1113->1115 1117 mae = 0.0 samples = 1 value = -1.015 1116->1117 1118 mae = 0.0 samples = 1 value = -0.863 1116->1118 1120 dcoilwtico ≤ 0.819 mae = 0.584 samples = 14 value = 0.224 1119->1120 1147 mae = 0.0 samples = 1 value = 1.43 1119->1147 1121 mae = 0.0 samples = 1 value = -0.739 1120->1121 1122 day_Wednesday ≤ 0.5 mae = 0.553 samples = 13 value = 0.247 1120->1122 1123 dcoilwtico ≤ 0.93 mae = 0.44 samples = 9 value = 0.201 1122->1123 1140 dcoilwtico ≤ 1.0 mae = 0.623 samples = 4 value = 0.792 1122->1140 1124 day_Friday ≤ 0.5 mae = 0.041 samples = 2 value = 0.37 1123->1124 1127 dcoilwtico ≤ 0.975 mae = 0.515 samples = 7 value = 0.181 1123->1127 1125 mae = 0.0 samples = 1 value = 0.33 1124->1125 1126 mae = 0.0 samples = 1 value = 0.411 1124->1126 1128 day_Monday ≤ 0.5 mae = 0.15 samples = 2 value = -0.792 1127->1128 1131 locale_name_None ≤ 0.5 mae = 0.327 samples = 5 value = 0.201 1127->1131 1129 mae = 0.0 samples = 1 value = -0.942 1128->1129 1130 mae = 0.0 samples = 1 value = -0.642 1128->1130 1132 mae = 0.0 samples = 1 value = 0.247 1131->1132 1133 day_Monday ≤ 0.5 mae = 0.398 samples = 4 value = 0.191 1131->1133 1134 mae = 0.0 samples = 1 value = 0.181 1133->1134 1135 dcoilwtico ≤ 1.014 mae = 0.523 samples = 3 value = 0.201 1133->1135 1136 mae = 0.0 samples = 1 value = 0.349 1135->1136 1137 dcoilwtico ≤ 1.038 mae = 0.711 samples = 2 value = -0.509 1135->1137 1138 mae = 0.0 samples = 1 value = -1.22 1137->1138 1139 mae = 0.0 samples = 1 value = 0.201 1137->1139 1141 dcoilwtico ≤ 0.91 mae = 0.574 samples = 3 value = 0.591 1140->1141 1146 mae = 0.0 samples = 1 value = 1.361 1140->1146 1142 mae = 0.0 samples = 1 value = 0.993 1141->1142 1143 locale_National ≤ 0.5 mae = 0.659 samples = 2 value = -0.068 1141->1143 1144 mae = 0.0 samples = 1 value = -0.727 1143->1144 1145 mae = 0.0 samples = 1 value = 0.591 1143->1145 1149 dcoilwtico ≤ 1.102 mae = 0.166 samples = 25 value = -0.969 1148->1149 1198 dcoilwtico ≤ 1.153 mae = 0.116 samples = 6 value = -0.741 1148->1198 1150 dcoilwtico ≤ 1.078 mae = 0.168 samples = 9 value = -1.012 1149->1150 1167 month_07 ≤ 0.5 mae = 0.155 samples = 16 value = -0.932 1149->1167 1151 day_Monday ≤ 0.5 mae = 0.434 samples = 3 value = -0.953 1150->1151 1156 day_Monday ≤ 0.5 mae = 0.025 samples = 6 value = -1.013 1150->1156 1152 dcoilwtico ≤ 1.065 mae = 0.048 samples = 2 value = -1.001 1151->1152 1155 mae = 0.0 samples = 1 value = 0.252 1151->1155 1153 mae = 0.0 samples = 1 value = -1.049 1152->1153 1154 mae = 0.0 samples = 1 value = -0.953 1152->1154 1157 month_08 ≤ 0.5 mae = 0.016 samples = 4 value = -1.001 1156->1157 1164 dcoilwtico ≤ 1.089 mae = 0.016 samples = 2 value = -1.054 1156->1164 1158 dcoilwtico ≤ 1.097 mae = 0.009 samples = 2 value = -0.98 1157->1158 1161 dcoilwtico ≤ 1.091 mae = 0.001 samples = 2 value = -1.013 1157->1161 1159 mae = 0.0 samples = 1 value = -0.971 1158->1159 1160 mae = 0.0 samples = 1 value = -0.989 1158->1160 1162 mae = 0.0 samples = 1 value = -1.012 1161->1162 1163 mae = 0.0 samples = 1 value = -1.013 1161->1163 1165 mae = 0.0 samples = 1 value = -1.07 1164->1165 1166 mae = 0.0 samples = 1 value = -1.038 1164->1166 1168 dcoilwtico ≤ 1.148 mae = 0.158 samples = 14 value = -0.912 1167->1168 1195 dcoilwtico ≤ 1.135 mae = 0.01 samples = 2 value = -1.046 1167->1195 1169 day_Monday ≤ 0.5 mae = 0.17 samples = 7 value = -0.836 1168->1169 1182 day_Monday ≤ 0.5 mae = 0.125 samples = 7 value = -0.952 1168->1182 1170 dcoilwtico ≤ 1.119 mae = 0.161 samples = 4 value = -0.743 1169->1170 1177 dcoilwtico ≤ 1.117 mae = 0.069 samples = 3 value = -0.953 1169->1177 1171 mae = 0.0 samples = 1 value = -0.282 1170->1171 1172 month_09 ≤ 0.5 mae = 0.046 samples = 3 value = -0.787 1170->1172 1173 dcoilwtico ≤ 1.133 mae = 0.025 samples = 2 value = -0.812 1172->1173 1176 mae = 0.0 samples = 1 value = -0.698 1172->1176 1174 mae = 0.0 samples = 1 value = -0.787 1173->1174 1175 mae = 0.0 samples = 1 value = -0.836 1173->1175 1178 locale_Local ≤ 0.5 mae = 0.027 samples = 2 value = -0.926 1177->1178 1181 mae = 0.0 samples = 1 value = -1.106 1177->1181 1179 mae = 0.0 samples = 1 value = -0.953 1178->1179 1180 mae = 0.0 samples = 1 value = -0.9 1178->1180 1183 month_08 ≤ 0.5 mae = 0.025 samples = 6 value = -0.961 1182->1183 1194 mae = 0.0 samples = 1 value = -0.225 1182->1194 1184 dcoilwtico ≤ 1.224 mae = 0.009 samples = 4 value = -0.971 1183->1184 1191 day_Wednesday ≤ 0.5 mae = 0.0 samples = 2 value = -0.912 1183->1191 1185 dcoilwtico ≤ 1.161 mae = 0.006 samples = 3 value = -0.972 1184->1185 1190 mae = 0.0 samples = 1 value = -0.952 1184->1190 1186 mae = 0.0 samples = 1 value = -0.969 1185->1186 1187 dcoilwtico ≤ 1.175 mae = 0.007 samples = 2 value = -0.979 1185->1187 1188 mae = 0.0 samples = 1 value = -0.987 1187->1188 1189 mae = 0.0 samples = 1 value = -0.972 1187->1189 1192 mae = 0.0 samples = 1 value = -0.913 1191->1192 1193 mae = 0.0 samples = 1 value = -0.912 1191->1193 1196 mae = 0.0 samples = 1 value = -1.056 1195->1196 1197 mae = 0.0 samples = 1 value = -1.036 1195->1197 1199 dcoilwtico ≤ 1.125 mae = 0.094 samples = 5 value = -0.713 1198->1199 1208 mae = 0.0 samples = 1 value = -0.938 1198->1208 1200 locale_National ≤ 0.5 mae = 0.029 samples = 3 value = -0.769 1199->1200 1205 dcoilwtico ≤ 1.143 mae = 0.009 samples = 2 value = -0.55 1199->1205 1201 dcoilwtico ≤ 1.103 mae = 0.015 samples = 2 value = -0.784 1200->1201 1204 mae = 0.0 samples = 1 value = -0.713 1200->1204 1202 mae = 0.0 samples = 1 value = -0.8 1201->1202 1203 mae = 0.0 samples = 1 value = -0.769 1201->1203 1206 mae = 0.0 samples = 1 value = -0.559 1205->1206 1207 mae = 0.0 samples = 1 value = -0.542 1205->1207 1212 month_01 ≤ 0.5 mae = 0.306 samples = 50 value = -1.0 1211->1212 1311 mae = 0.0 samples = 1 value = -0.04 1211->1311 1213 dcoilwtico ≤ 0.942 mae = 0.274 samples = 47 value = -1.002 1212->1213 1306 dcoilwtico ≤ 0.701 mae = 0.497 samples = 3 value = -0.085 1212->1306 1214 month_09 ≤ 0.5 mae = 0.285 samples = 28 value = -0.972 1213->1214 1269 month_06 ≤ 0.5 mae = 0.227 samples = 19 value = -1.131 1213->1269 1215 month_08 ≤ 0.5 mae = 0.242 samples = 27 value = -0.974 1214->1215 1268 mae = 0.0 samples = 1 value = 0.492 1214->1268 1216 dcoilwtico ≤ 0.82 mae = 0.24 samples = 24 value = -0.999 1215->1216 1263 dcoilwtico ≤ 0.626 mae = 0.044 samples = 3 value = -0.741 1215->1263 1217 dcoilwtico ≤ 0.569 mae = 0.189 samples = 17 value = -1.039 1216->1217 1250 month_07 ≤ 0.5 mae = 0.304 samples = 7 value = -0.885 1216->1250 1218 dcoilwtico ≤ 0.555 mae = 0.035 samples = 4 value = -0.99 1217->1218 1225 type_No_Holiday ≤ 0.5 mae = 0.199 samples = 13 value = -1.181 1217->1225 1219 mae = 0.0 samples = 1 value = -1.039 1218->1219 1220 dcoilwtico ≤ 0.557 mae = 0.023 samples = 3 value = -0.968 1218->1220 1221 dcoilwtico ≤ 0.556 mae = 0.011 samples = 2 value = -0.956 1220->1221 1224 mae = 0.0 samples = 1 value = -1.012 1220->1224 1222 mae = 0.0 samples = 1 value = -0.968 1221->1222 1223 mae = 0.0 samples = 1 value = -0.945 1221->1223 1226 mae = 0.0 samples = 1 value = -0.791 1225->1226 1227 dcoilwtico ≤ 0.792 mae = 0.184 samples = 12 value = -1.196 1225->1227 1228 month_04 ≤ 0.5 mae = 0.181 samples = 11 value = -1.211 1227->1228 1249 mae = 0.0 samples = 1 value = -0.996 1227->1249 1229 month_10 ≤ 0.5 mae = 0.178 samples = 10 value = -1.227 1228->1229 1248 mae = 0.0 samples = 1 value = -1.002 1228->1248 1230 dcoilwtico ≤ 0.646 mae = 0.204 samples = 8 value = -1.196 1229->1230 1245 dcoilwtico ≤ 0.747 mae = 0.027 samples = 2 value = -1.284 1229->1245 1231 month_05 ≤ 0.5 mae = 0.175 samples = 3 value = -1.243 1230->1231 1236 month_05 ≤ 0.5 mae = 0.197 samples = 5 value = -1.18 1230->1236 1232 mae = 0.0 samples = 1 value = -1.736 1231->1232 1233 dcoilwtico ≤ 0.595 mae = 0.016 samples = 2 value = -1.227 1231->1233 1234 mae = 0.0 samples = 1 value = -1.211 1233->1234 1235 mae = 0.0 samples = 1 value = -1.243 1233->1235 1237 dcoilwtico ≤ 0.705 mae = 0.087 samples = 4 value = -1.18 1236->1237 1244 mae = 0.0 samples = 1 value = -0.541 1236->1244 1238 dcoilwtico ≤ 0.694 mae = 0.069 samples = 2 value = -1.25 1237->1238 1241 dcoilwtico ≤ 0.742 mae = 0.103 samples = 2 value = -1.077 1237->1241 1239 mae = 0.0 samples = 1 value = -1.181 1238->1239 1240 mae = 0.0 samples = 1 value = -1.319 1238->1240 1242 mae = 0.0 samples = 1 value = -0.974 1241->1242 1243 mae = 0.0 samples = 1 value = -1.18 1241->1243 1246 mae = 0.0 samples = 1 value = -1.311 1245->1246 1247 mae = 0.0 samples = 1 value = -1.257 1245->1247 1251 month_10 ≤ 0.5 mae = 0.201 samples = 6 value = -0.9 1250->1251 1262 mae = 0.0 samples = 1 value = 0.036 1250->1262 1252 month_05 ≤ 0.5 mae = 0.22 samples = 5 value = -0.915 1251->1252 1261 mae = 0.0 samples = 1 value = -0.809 1251->1261 1253 dcoilwtico ≤ 0.899 mae = 0.339 samples = 3 value = -0.969 1252->1253 1258 dcoilwtico ≤ 0.869 mae = 0.015 samples = 2 value = -0.9 1252->1258 1254 mae = 0.0 samples = 1 value = -1.132 1253->1254 1255 dcoilwtico ≤ 0.919 mae = 0.427 samples = 2 value = -0.542 1253->1255 1256 mae = 0.0 samples = 1 value = -0.116 1255->1256 1257 mae = 0.0 samples = 1 value = -0.969 1255->1257 1259 mae = 0.0 samples = 1 value = -0.885 1258->1259 1260 mae = 0.0 samples = 1 value = -0.915 1258->1260 1264 mae = 0.0 samples = 1 value = -0.658 1263->1264 1265 locale_None ≤ 0.5 mae = 0.024 samples = 2 value = -0.766 1263->1265 1266 mae = 0.0 samples = 1 value = -0.79 1265->1266 1267 mae = 0.0 samples = 1 value = -0.741 1265->1267 1270 month_09 ≤ 0.5 mae = 0.222 samples = 15 value = -1.176 1269->1270 1299 dcoilwtico ≤ 1.005 mae = 0.114 samples = 4 value = -0.923 1269->1299 1271 month_10 ≤ 0.5 mae = 0.246 samples = 11 value = -1.157 1270->1271 1292 dcoilwtico ≤ 1.167 mae = 0.113 samples = 4 value = -1.251 1270->1292 1272 month_08 ≤ 0.5 mae = 0.263 samples = 10 value = -1.144 1271->1272 1291 mae = 0.0 samples = 1 value = -1.233 1271->1291 1273 dcoilwtico ≤ 0.999 mae = 0.346 samples = 7 value = -1.102 1272->1273 1286 dcoilwtico ≤ 1.161 mae = 0.027 samples = 3 value = -1.176 1272->1286 1274 month_07 ≤ 0.5 mae = 0.034 samples = 3 value = -1.178 1273->1274 1279 dcoilwtico ≤ 1.055 mae = 0.509 samples = 4 value = -0.537 1273->1279 1275 mae = 0.0 samples = 1 value = -1.102 1274->1275 1276 dcoilwtico ≤ 0.968 mae = 0.013 samples = 2 value = -1.191 1274->1276 1277 mae = 0.0 samples = 1 value = -1.178 1276->1277 1278 mae = 0.0 samples = 1 value = -1.205 1276->1278 1280 dcoilwtico ≤ 1.029 mae = 0.029 samples = 2 value = -0.047 1279->1280 1283 transferred_None ≤ 0.5 mae = 0.066 samples = 2 value = -1.065 1279->1283 1281 mae = 0.0 samples = 1 value = -0.018 1280->1281 1282 mae = 0.0 samples = 1 value = -0.075 1280->1282 1284 mae = 0.0 samples = 1 value = -1.131 1283->1284 1285 mae = 0.0 samples = 1 value = -0.999 1283->1285 1287 dcoilwtico ≤ 1.083 mae = 0.032 samples = 2 value = -1.207 1286->1287 1290 mae = 0.0 samples = 1 value = -1.157 1286->1290 1288 mae = 0.0 samples = 1 value = -1.176 1287->1288 1289 mae = 0.0 samples = 1 value = -1.239 1287->1289 1293 dcoilwtico ≤ 1.01 mae = 0.013 samples = 3 value = -1.251 1292->1293 1298 mae = 0.0 samples = 1 value = -0.839 1292->1298 1294 mae = 0.0 samples = 1 value = -1.291 1293->1294 1295 dcoilwtico ≤ 1.098 mae = 0.0 samples = 2 value = -1.251 1293->1295 1296 mae = 0.0 samples = 1 value = -1.251 1295->1296 1297 mae = 0.0 samples = 1 value = -1.251 1295->1297 1300 mae = 0.0 samples = 1 value = -0.696 1299->1300 1301 dcoilwtico ≤ 1.074 mae = 0.068 samples = 3 value = -0.948 1299->1301 1302 mae = 0.0 samples = 1 value = -1.102 1301->1302 1303 dcoilwtico ≤ 1.114 mae = 0.025 samples = 2 value = -0.923 1301->1303 1304 mae = 0.0 samples = 1 value = -0.948 1303->1304 1305 mae = 0.0 samples = 1 value = -0.898 1303->1305 1307 dcoilwtico ≤ 0.636 mae = 0.127 samples = 2 value = 0.042 1306->1307 1310 mae = 0.0 samples = 1 value = -1.323 1306->1310 1308 mae = 0.0 samples = 1 value = -0.085 1307->1308 1309 mae = 0.0 samples = 1 value = 0.169 1307->1309 1313 dcoilwtico ≤ 0.587 mae = 0.543 samples = 9 value = -0.539 1312->1313 1330 type_Additional ≤ 0.5 mae = 0.298 samples = 44 value = -1.142 1312->1330 1314 dcoilwtico ≤ 0.564 mae = 0.458 samples = 7 value = -0.706 1313->1314 1327 month_09 ≤ 0.5 mae = 0.013 samples = 2 value = 0.218 1313->1327 1315 month_01 ≤ 0.5 mae = 0.273 samples = 2 value = -0.266 1314->1315 1318 month_09 ≤ 0.5 mae = 0.372 samples = 5 value = -1.174 1314->1318 1316 mae = 0.0 samples = 1 value = -0.539 1315->1316 1317 mae = 0.0 samples = 1 value = 0.007 1315->1317 1319 dcoilwtico ≤ 0.578 mae = 0.17 samples = 4 value = -1.214 1318->1319 1326 mae = 0.0 samples = 1 value = 0.006 1318->1326 1320 month_11 ≤ 0.5 mae = 0.043 samples = 3 value = -1.255 1319->1320 1325 mae = 0.0 samples = 1 value = -0.706 1319->1325 1321 month_01 ≤ 0.5 mae = 0.024 samples = 2 value = -1.279 1320->1321 1324 mae = 0.0 samples = 1 value = -1.174 1320->1324 1322 mae = 0.0 samples = 1 value = -1.303 1321->1322 1323 mae = 0.0 samples = 1 value = -1.255 1321->1323 1328 mae = 0.0 samples = 1 value = 0.205 1327->1328 1329 mae = 0.0 samples = 1 value = 0.231 1327->1329 1331 dcoilwtico ≤ 0.713 mae = 0.286 samples = 43 value = -1.15 1330->1331 1416 mae = 0.0 samples = 1 value = -0.318 1330->1416 1332 month_08 ≤ 0.5 mae = 0.173 samples = 10 value = -1.274 1331->1332 1351 dcoilwtico ≤ 0.72 mae = 0.301 samples = 33 value = -1.073 1331->1351 1333 dcoilwtico ≤ 0.641 mae = 0.094 samples = 8 value = -1.327 1332->1333 1348 dcoilwtico ≤ 0.665 mae = 0.012 samples = 2 value = -0.805 1332->1348 1334 mae = 0.0 samples = 1 value = -1.5 1333->1334 1335 dcoilwtico ≤ 0.673 mae = 0.078 samples = 7 value = -1.294 1333->1335 1336 month_01 ≤ 0.5 mae = 0.037 samples = 3 value = -1.226 1335->1336 1341 dcoilwtico ≤ 0.685 mae = 0.059 samples = 4 value = -1.388 1335->1341 1337 month_02 ≤ 0.5 mae = 0.022 samples = 2 value = -1.203 1336->1337 1340 mae = 0.0 samples = 1 value = -1.294 1336->1340 1338 mae = 0.0 samples = 1 value = -1.226 1337->1338 1339 mae = 0.0 samples = 1 value = -1.181 1337->1339 1342 mae = 0.0 samples = 1 value = -1.436 1341->1342 1343 dcoilwtico ≤ 0.7 mae = 0.053 samples = 3 value = -1.361 1341->1343 1344 month_06 ≤ 0.5 mae = 0.053 samples = 2 value = -1.308 1343->1344 1347 mae = 0.0 samples = 1 value = -1.415 1343->1347 1345 mae = 0.0 samples = 1 value = -1.361 1344->1345 1346 mae = 0.0 samples = 1 value = -1.255 1344->1346 1349 mae = 0.0 samples = 1 value = -0.817 1348->1349 1350 mae = 0.0 samples = 1 value = -0.794 1348->1350 1352 mae = 0.0 samples = 1 value = 0.487 1351->1352 1353 dcoilwtico ≤ 0.892 mae = 0.262 samples = 32 value = -1.093 1351->1353 1354 locale_National ≤ 0.5 mae = 0.4 samples = 9 value = -0.955 1353->1354 1371 month_06 ≤ 0.5 mae = 0.173 samples = 23 value = -1.166 1353->1371 1355 month_07 ≤ 0.5 mae = 0.329 samples = 8 value = -0.957 1354->1355 1370 mae = 0.0 samples = 1 value = 0.02 1354->1370 1356 dcoilwtico ≤ 0.857 mae = 0.241 samples = 7 value = -0.958 1355->1356 1369 mae = 0.0 samples = 1 value = -0.014 1355->1369 1357 dcoilwtico ≤ 0.848 mae = 0.207 samples = 6 value = -0.975 1356->1357 1368 mae = 0.0 samples = 1 value = -0.52 1356->1368 1358 dcoilwtico ≤ 0.738 mae = 0.211 samples = 5 value = -0.958 1357->1358 1367 mae = 0.0 samples = 1 value = -1.15 1357->1367 1359 dcoilwtico ≤ 0.727 mae = 0.208 samples = 2 value = -1.2 1358->1359 1362 month_01 ≤ 0.5 mae = 0.189 samples = 3 value = -0.955 1358->1362 1360 mae = 0.0 samples = 1 value = -0.992 1359->1360 1361 mae = 0.0 samples = 1 value = -1.408 1359->1361 1363 dcoilwtico ≤ 0.793 mae = 0.002 samples = 2 value = -0.957 1362->1363 1366 mae = 0.0 samples = 1 value = -0.391 1362->1366 1364 mae = 0.0 samples = 1 value = -0.955 1363->1364 1365 mae = 0.0 samples = 1 value = -0.958 1363->1365 1372 dcoilwtico ≤ 1.029 mae = 0.178 samples = 20 value = -1.204 1371->1372 1411 type_No_Holiday ≤ 0.5 mae = 0.019 samples = 3 value = -1.057 1371->1411 1373 dcoilwtico ≤ 1.02 mae = 0.208 samples = 12 value = -1.123 1372->1373 1396 month_07 ≤ 0.5 mae = 0.091 samples = 8 value = -1.284 1372->1396 1374 month_09 ≤ 0.5 mae = 0.105 samples = 11 value = -1.134 1373->1374 1395 mae = 0.0 samples = 1 value = 0.197 1373->1395 1375 dcoilwtico ≤ 1.01 mae = 0.093 samples = 10 value = -1.123 1374->1375 1394 mae = 0.0 samples = 1 value = -1.362 1374->1394 1376 month_08 ≤ 0.5 mae = 0.079 samples = 9 value = -1.134 1375->1376 1393 mae = 0.0 samples = 1 value = -0.911 1375->1393 1377 dcoilwtico ≤ 1.004 mae = 0.07 samples = 8 value = -1.123 1376->1377 1392 mae = 0.0 samples = 1 value = -1.283 1376->1392 1378 dcoilwtico ≤ 0.966 mae = 0.065 samples = 7 value = -1.112 1377->1378 1391 mae = 0.0 samples = 1 value = -1.214 1377->1391 1379 month_10 ≤ 0.5 mae = 0.062 samples = 5 value = -1.134 1378->1379 1388 dcoilwtico ≤ 0.983 mae = 0.002 samples = 2 value = -1.05 1378->1388 1380 month_07 ≤ 0.5 mae = 0.037 samples = 4 value = -1.123 1379->1380 1387 mae = 0.0 samples = 1 value = -1.297 1379->1387 1381 dcoilwtico ≤ 0.933 mae = 0.022 samples = 3 value = -1.112 1380->1381 1386 mae = 0.0 samples = 1 value = -1.193 1380->1386 1382 mae = 0.0 samples = 1 value = -1.067 1381->1382 1383 dcoilwtico ≤ 0.953 mae = 0.011 samples = 2 value = -1.123 1381->1383 1384 mae = 0.0 samples = 1 value = -1.134 1383->1384 1385 mae = 0.0 samples = 1 value = -1.112 1383->1385 1389 mae = 0.0 samples = 1 value = -1.053 1388->1389 1390 mae = 0.0 samples = 1 value = -1.048 1388->1390 1397 dcoilwtico ≤ 1.126 mae = 0.109 samples = 5 value = -1.229 1396->1397 1406 dcoilwtico ≤ 1.11 mae = 0.013 samples = 3 value = -1.316 1396->1406 1398 month_08 ≤ 0.5 mae = 0.058 samples = 2 value = -1.328 1397->1398 1401 month_08 ≤ 0.5 mae = 0.095 samples = 3 value = -1.166 1397->1401 1399 mae = 0.0 samples = 1 value = -1.386 1398->1399 1400 mae = 0.0 samples = 1 value = -1.27 1398->1400 1402 dcoilwtico ≤ 1.189 mae = 0.032 samples = 2 value = -1.197 1401->1402 1405 mae = 0.0 samples = 1 value = -0.945 1401->1405 1403 mae = 0.0 samples = 1 value = -1.229 1402->1403 1404 mae = 0.0 samples = 1 value = -1.166 1402->1404 1407 locale_Local ≤ 0.5 mae = 0.009 samples = 2 value = -1.308 1406->1407 1410 mae = 0.0 samples = 1 value = -1.337 1406->1410 1408 mae = 0.0 samples = 1 value = -1.299 1407->1408 1409 mae = 0.0 samples = 1 value = -1.316 1407->1409 1412 mae = 0.0 samples = 1 value = -1.073 1411->1412 1413 dcoilwtico ≤ 1.113 mae = 0.02 samples = 2 value = -1.036 1411->1413 1414 mae = 0.0 samples = 1 value = -1.057 1413->1414 1415 mae = 0.0 samples = 1 value = -1.016 1413->1415 1418 day_Tuesday ≤ 0.5 mae = 0.136 samples = 7 value = -1.055 1417->1418 1431 day_Thursday ≤ 0.5 mae = 0.304 samples = 13 value = 0.199 1417->1431 1419 day_Friday ≤ 0.5 mae = 0.07 samples = 6 value = -1.047 1418->1419 1430 mae = 0.0 samples = 1 value = -1.585 1418->1430 1420 dcoilwtico ≤ 0.588 mae = 0.029 samples = 3 value = -1.104 1419->1420 1425 dcoilwtico ≤ 0.638 mae = 0.074 samples = 3 value = -1.024 1419->1425 1421 mae = 0.0 samples = 1 value = -1.055 1420->1421 1422 dcoilwtico ≤ 0.661 mae = 0.019 samples = 2 value = -1.123 1420->1422 1423 mae = 0.0 samples = 1 value = -1.143 1422->1423 1424 mae = 0.0 samples = 1 value = -1.104 1422->1424 1426 dcoilwtico ≤ 0.559 mae = 0.007 samples = 2 value = -1.032 1425->1426 1429 mae = 0.0 samples = 1 value = -0.817 1425->1429 1427 mae = 0.0 samples = 1 value = -1.039 1426->1427 1428 mae = 0.0 samples = 1 value = -1.024 1426->1428 1432 day_Wednesday ≤ 0.5 mae = 0.247 samples = 9 value = 0.219 1431->1432 1449 dcoilwtico ≤ 0.895 mae = 0.183 samples = 4 value = -0.292 1431->1449 1433 dcoilwtico ≤ 0.866 mae = 0.199 samples = 7 value = 0.199 1432->1433 1446 dcoilwtico ≤ 0.811 mae = 0.029 samples = 2 value = 0.621 1432->1446 1434 dcoilwtico ≤ 0.8 mae = 0.103 samples = 4 value = 0.025 1433->1434 1441 locale_name_None ≤ 0.5 mae = 0.158 samples = 3 value = 0.368 1433->1441 1435 mae = 0.0 samples = 1 value = 0.219 1434->1435 1436 dcoilwtico ≤ 0.839 mae = 0.071 samples = 3 value = 0.02 1434->1436 1437 day_Monday ≤ 0.5 mae = 0.102 samples = 2 value = -0.082 1436->1437 1440 mae = 0.0 samples = 1 value = 0.03 1436->1440 1438 mae = 0.0 samples = 1 value = -0.184 1437->1438 1439 mae = 0.0 samples = 1 value = 0.02 1437->1439 1442 mae = 0.0 samples = 1 value = 0.674 1441->1442 1443 dcoilwtico ≤ 0.893 mae = 0.085 samples = 2 value = 0.284 1441->1443 1444 mae = 0.0 samples = 1 value = 0.199 1443->1444 1445 mae = 0.0 samples = 1 value = 0.368 1443->1445 1447 mae = 0.0 samples = 1 value = 0.65 1446->1447 1448 mae = 0.0 samples = 1 value = 0.593 1446->1448 1450 dcoilwtico ≤ 0.851 mae = 0.048 samples = 3 value = -0.296 1449->1450 1455 mae = 0.0 samples = 1 value = 0.293 1449->1455 1451 dcoilwtico ≤ 0.795 mae = 0.004 samples = 2 value = -0.292 1450->1451 1454 mae = 0.0 samples = 1 value = -0.433 1450->1454 1452 mae = 0.0 samples = 1 value = -0.289 1451->1452 1453 mae = 0.0 samples = 1 value = -0.296 1451->1453 1457 type_Holiday ≤ 0.5 mae = 0.22 samples = 13 value = -0.583 1456->1457 1482 dcoilwtico ≤ 0.64 mae = 0.262 samples = 4 value = 0.24 1456->1482 1458 locale_Local ≤ 0.5 mae = 0.196 samples = 12 value = -0.594 1457->1458 1481 mae = 0.0 samples = 1 value = -0.076 1457->1481 1459 locale_National ≤ 0.5 mae = 0.176 samples = 11 value = -0.583 1458->1459 1480 mae = 0.0 samples = 1 value = -1.007 1458->1480 1460 dcoilwtico ≤ 0.716 mae = 0.191 samples = 8 value = -0.482 1459->1460 1475 dcoilwtico ≤ 0.77 mae = 0.066 samples = 3 value = -0.703 1459->1475 1461 day_Friday ≤ 0.5 mae = 0.115 samples = 4 value = -0.572 1460->1461 1468 dcoilwtico ≤ 0.797 mae = 0.209 samples = 4 value = -0.273 1460->1468 1462 day_Thursday ≤ 0.5 mae = 0.051 samples = 3 value = -0.604 1461->1462 1467 mae = 0.0 samples = 1 value = -0.296 1461->1467 1463 day_Wednesday ≤ 0.5 mae = 0.032 samples = 2 value = -0.572 1462->1463 1466 mae = 0.0 samples = 1 value = -0.692 1462->1466 1464 mae = 0.0 samples = 1 value = -0.539 1463->1464 1465 mae = 0.0 samples = 1 value = -0.604 1463->1465 1469 day_Tuesday ≤ 0.5 mae = 0.105 samples = 3 value = -0.121 1468->1469 1474 mae = 0.0 samples = 1 value = -0.642 1468->1474 1470 dcoilwtico ≤ 0.741 mae = 0.006 samples = 2 value = -0.116 1469->1470 1473 mae = 0.0 samples = 1 value = -0.425 1469->1473 1471 mae = 0.0 samples = 1 value = -0.121 1470->1471 1472 mae = 0.0 samples = 1 value = -0.11 1470->1472 1476 mae = 0.0 samples = 1 value = -0.782 1475->1476 1477 day_Thursday ≤ 0.5 mae = 0.06 samples = 2 value = -0.643 1475->1477 1478 mae = 0.0 samples = 1 value = -0.583 1477->1478 1479 mae = 0.0 samples = 1 value = -0.703 1477->1479 1483 mae = 0.0 samples = 1 value = 0.447 1482->1483 1484 locale_National ≤ 0.5 mae = 0.218 samples = 3 value = 0.053 1482->1484 1485 dcoilwtico ≤ 0.75 mae = 0.14 samples = 2 value = -0.087 1484->1485 1488 mae = 0.0 samples = 1 value = 0.427 1484->1488 1486 mae = 0.0 samples = 1 value = -0.227 1485->1486 1487 mae = 0.0 samples = 1 value = 0.053 1485->1487 1490 mae = 0.0 samples = 1 value = 2.525 1489->1490 1491 month_03 ≤ 0.5 mae = 0.427 samples = 61 value = 0.028 1489->1491 1492 dcoilwtico ≤ 1.011 mae = 0.361 samples = 54 value = 0.026 1491->1492 1599 dcoilwtico ≤ 0.751 mae = 0.758 samples = 7 value = 1.289 1491->1599 1493 type_Event ≤ 0.5 mae = 0.349 samples = 39 value = 0.066 1492->1493 1570 dcoilwtico ≤ 1.141 mae = 0.323 samples = 15 value = -0.231 1492->1570 1494 dcoilwtico ≤ 1.003 mae = 0.318 samples = 38 value = 0.06 1493->1494 1569 mae = 0.0 samples = 1 value = 1.598 1493->1569 1495 month_08 ≤ 0.5 mae = 0.293 samples = 37 value = 0.054 1494->1495 1568 mae = 0.0 samples = 1 value = 1.318 1494->1568 1496 dcoilwtico ≤ 0.676 mae = 0.289 samples = 33 value = 0.025 1495->1496 1561 dcoilwtico ≤ 0.641 mae = 0.065 samples = 4 value = 0.347 1495->1561 1497 month_11 ≤ 0.5 mae = 0.26 samples = 13 value = -0.17 1496->1497 1522 month_01 ≤ 0.5 mae = 0.28 samples = 20 value = 0.069 1496->1522 1498 dcoilwtico ≤ 0.641 mae = 0.222 samples = 9 value = -0.313 1497->1498 1515 dcoilwtico ≤ 0.579 mae = 0.088 samples = 4 value = 0.147 1497->1515 1499 month_01 ≤ 0.5 mae = 0.085 samples = 3 value = -0.517 1498->1499 1504 month_02 ≤ 0.5 mae = 0.197 samples = 6 value = -0.205 1498->1504 1500 mae = 0.0 samples = 1 value = -0.67 1499->1500 1501 type_No_Holiday ≤ 0.5 mae = 0.05 samples = 2 value = -0.467 1499->1501 1502 mae = 0.0 samples = 1 value = -0.517 1501->1502 1503 mae = 0.0 samples = 1 value = -0.416 1501->1503 1505 dcoilwtico ≤ 0.648 mae = 0.128 samples = 5 value = -0.17 1504->1505 1514 mae = 0.0 samples = 1 value = -0.712 1504->1514 1506 locale_name_None ≤ 0.5 mae = 0.02 samples = 2 value = 0.045 1505->1506 1509 month_06 ≤ 0.5 mae = 0.048 samples = 3 value = -0.239 1505->1509 1507 mae = 0.0 samples = 1 value = 0.066 1506->1507 1508 mae = 0.0 samples = 1 value = 0.025 1506->1508 1510 month_01 ≤ 0.5 mae = 0.034 samples = 2 value = -0.205 1509->1510 1513 mae = 0.0 samples = 1 value = -0.313 1509->1513 1511 mae = 0.0 samples = 1 value = -0.17 1510->1511 1512 mae = 0.0 samples = 1 value = -0.239 1510->1512 1516 mae = 0.0 samples = 1 value = 0.236 1515->1516 1517 dcoilwtico ≤ 0.6 mae = 0.086 samples = 3 value = 0.142 1515->1517 1518 mae = 0.0 samples = 1 value = -0.108 1517->1518 1519 locale_None ≤ 0.5 mae = 0.005 samples = 2 value = 0.147 1517->1519 1520 mae = 0.0 samples = 1 value = 0.142 1519->1520 1521 mae = 0.0 samples = 1 value = 0.151 1519->1521 1523 type_Additional ≤ 0.5 mae = 0.227 samples = 19 value = 0.054 1522->1523 1560 mae = 0.0 samples = 1 value = 1.341 1522->1560 1524 month_10 ≤ 0.5 mae = 0.205 samples = 18 value = 0.047 1523->1524 1559 mae = 0.0 samples = 1 value = 0.674 1523->1559 1525 month_07 ≤ 0.5 mae = 0.181 samples = 15 value = 0.085 1524->1525 1554 dcoilwtico ≤ 0.886 mae = 0.163 samples = 3 value = -0.299 1524->1554 1526 type_No_Holiday ≤ 0.5 mae = 0.166 samples = 14 value = 0.096 1525->1526 1553 mae = 0.0 samples = 1 value = -0.308 1525->1553 1527 mae = 0.0 samples = 1 value = -0.229 1526->1527 1528 dcoilwtico ≤ 0.696 mae = 0.153 samples = 13 value = 0.107 1526->1528 1529 mae = 0.0 samples = 1 value = 0.281 1528->1529 1530 dcoilwtico ≤ 0.705 mae = 0.151 samples = 12 value = 0.096 1528->1530 1531 mae = 0.0 samples = 1 value = -0.247 1530->1531 1532 month_05 ≤ 0.5 mae = 0.133 samples = 11 value = 0.107 1530->1532 1533 dcoilwtico ≤ 0.85 mae = 0.138 samples = 8 value = 0.069 1532->1533 1548 dcoilwtico ≤ 0.883 mae = 0.074 samples = 3 value = 0.154 1532->1548 1534 dcoilwtico ≤ 0.777 mae = 0.135 samples = 4 value = 0.031 1533->1534 1541 month_06 ≤ 0.5 mae = 0.119 samples = 4 value = 0.096 1533->1541 1535 dcoilwtico ≤ 0.711 mae = 0.146 samples = 3 value = 0.039 1534->1535 1540 mae = 0.0 samples = 1 value = -0.064 1534->1540 1536 mae = 0.0 samples = 1 value = 0.023 1535->1536 1537 month_12 ≤ 0.5 mae = 0.211 samples = 2 value = 0.25 1535->1537 1538 mae = 0.0 samples = 1 value = 0.462 1537->1538 1539 mae = 0.0 samples = 1 value = 0.039 1537->1539 1542 dcoilwtico ≤ 0.87 mae = 0.151 samples = 3 value = 0.085 1541->1542 1547 mae = 0.0 samples = 1 value = 0.107 1541->1547 1543 dcoilwtico ≤ 0.868 mae = 0.211 samples = 2 value = 0.296 1542->1543 1546 mae = 0.0 samples = 1 value = 0.054 1542->1546 1544 mae = 0.0 samples = 1 value = 0.085 1543->1544 1545 mae = 0.0 samples = 1 value = 0.507 1543->1545 1549 mae = 0.0 samples = 1 value = 0.351 1548->1549 1550 dcoilwtico ≤ 0.952 mae = 0.013 samples = 2 value = 0.141 1548->1550 1551 mae = 0.0 samples = 1 value = 0.154 1550->1551 1552 mae = 0.0 samples = 1 value = 0.128 1550->1552 1555 dcoilwtico ≤ 0.794 mae = 0.151 samples = 2 value = -0.148 1554->1555 1558 mae = 0.0 samples = 1 value = -0.488 1554->1558 1556 mae = 0.0 samples = 1 value = -0.299 1555->1556 1557 mae = 0.0 samples = 1 value = 0.002 1555->1557 1562 mae = 0.0 samples = 1 value = 0.254 1561->1562 1563 dcoilwtico ≤ 0.699 mae = 0.054 samples = 3 value = 0.351 1561->1563 1564 mae = 0.0 samples = 1 value = 0.506 1563->1564 1565 dcoilwtico ≤ 0.728 mae = 0.004 samples = 2 value = 0.347 1563->1565 1566 mae = 0.0 samples = 1 value = 0.351 1565->1566 1567 mae = 0.0 samples = 1 value = 0.343 1565->1567 1571 locale_National ≤ 0.5 mae = 0.293 samples = 9 value = -0.341 1570->1571 1588 dcoilwtico ≤ 1.144 mae = 0.246 samples = 6 value = 0.054 1570->1588 1572 dcoilwtico ≤ 1.019 mae = 0.3 samples = 7 value = -0.474 1571->1572 1585 dcoilwtico ≤ 1.066 mae = 0.053 samples = 2 value = -0.139 1571->1585 1573 mae = 0.0 samples = 1 value = -0.282 1572->1573 1574 type_Holiday ≤ 0.5 mae = 0.318 samples = 6 value = -0.491 1572->1574 1575 dcoilwtico ≤ 1.041 mae = 0.365 samples = 5 value = -0.474 1574->1575 1584 mae = 0.0 samples = 1 value = -0.559 1574->1584 1576 mae = 0.0 samples = 1 value = -0.546 1575->1576 1577 dcoilwtico ≤ 1.067 mae = 0.439 samples = 4 value = -0.407 1575->1577 1578 mae = 0.0 samples = 1 value = 1.114 1577->1578 1579 month_07 ≤ 0.5 mae = 0.055 samples = 3 value = -0.474 1577->1579 1580 dcoilwtico ≤ 1.128 mae = 0.067 samples = 2 value = -0.407 1579->1580 1583 mae = 0.0 samples = 1 value = -0.507 1579->1583 1581 mae = 0.0 samples = 1 value = -0.341 1580->1581 1582 mae = 0.0 samples = 1 value = -0.474 1580->1582 1586 mae = 0.0 samples = 1 value = -0.086 1585->1586 1587 mae = 0.0 samples = 1 value = -0.192 1585->1587 1589 mae = 0.0 samples = 1 value = 0.682 1588->1589 1590 month_07 ≤ 0.5 mae = 0.164 samples = 5 value = 0.028 1588->1590 1591 month_06 ≤ 0.5 mae = 0.1 samples = 4 value = 0.054 1590->1591 1598 mae = 0.0 samples = 1 value = -0.394 1590->1598 1592 dcoilwtico ≤ 1.161 mae = 0.104 samples = 3 value = 0.028 1591->1592 1597 mae = 0.0 samples = 1 value = 0.116 1591->1597 1593 mae = 0.0 samples = 1 value = -0.231 1592->1593 1594 month_08 ≤ 0.5 mae = 0.026 samples = 2 value = 0.054 1592->1594 1595 mae = 0.0 samples = 1 value = 0.08 1594->1595 1596 mae = 0.0 samples = 1 value = 0.028 1594->1596 1600 dcoilwtico ≤ 0.644 mae = 0.153 samples = 3 value = -0.142 1599->1600 1605 dcoilwtico ≤ 0.81 mae = 0.172 samples = 4 value = 1.47 1599->1605 1601 dcoilwtico ≤ 0.569 mae = 0.054 samples = 2 value = -0.088 1600->1601 1604 mae = 0.0 samples = 1 value = -0.492 1600->1604 1602 mae = 0.0 samples = 1 value = -0.142 1601->1602 1603 mae = 0.0 samples = 1 value = -0.034 1601->1603 1606 mae = 0.0 samples = 1 value = 1.331 1605->1606 1607 dcoilwtico ≤ 0.88 mae = 0.137 samples = 3 value = 1.608 1605->1607 1608 mae = 0.0 samples = 1 value = 1.7 1607->1608 1609 dcoilwtico ≤ 0.957 mae = 0.16 samples = 2 value = 1.449 1607->1609 1610 mae = 0.0 samples = 1 value = 1.289 1609->1610 1611 mae = 0.0 samples = 1 value = 1.608 1609->1611 1613 month_03 ≤ 0.5 mae = 0.572 samples = 64 value = 0.114 1612->1613 1740 mae = 0.0 samples = 1 value = 2.652 1612->1740 1614 type_Event ≤ 0.5 mae = 0.491 samples = 58 value = 0.096 1613->1614 1729 dcoilwtico ≤ 0.683 mae = 1.113 samples = 6 value = 1.447 1613->1729 1615 dcoilwtico ≤ 1.072 mae = 0.473 samples = 55 value = 0.101 1614->1615 1724 month_05 ≤ 0.5 mae = 0.625 samples = 3 value = -0.495 1614->1724 1616 dcoilwtico ≤ 1.029 mae = 0.49 samples = 46 value = 0.114 1615->1616 1707 dcoilwtico ≤ 1.126 mae = 0.318 samples = 9 value = -0.309 1615->1707 1617 dcoilwtico ≤ 1.022 mae = 0.463 samples = 43 value = 0.101 1616->1617 1702 month_07 ≤ 0.5 mae = 0.369 samples = 3 value = 1.338 1616->1702 1618 month_02 ≤ 0.5 mae = 0.461 samples = 42 value = 0.103 1617->1618 1701 mae = 0.0 samples = 1 value = -0.478 1617->1701 1619 month_06 ≤ 0.5 mae = 0.462 samples = 35 value = 0.179 1618->1619 1688 dcoilwtico ≤ 0.69 mae = 0.367 samples = 7 value = -0.068 1618->1688 1620 dcoilwtico ≤ 0.731 mae = 0.467 samples = 29 value = 0.22 1619->1620 1677 dcoilwtico ≤ 0.853 mae = 0.321 samples = 6 value = -0.11 1619->1677 1621 month_05 ≤ 0.5 mae = 0.486 samples = 14 value = 0.286 1620->1621 1648 month_12 ≤ 0.5 mae = 0.415 samples = 15 value = 0.106 1620->1648 1622 locale_None ≤ 0.5 mae = 0.466 samples = 12 value = 0.291 1621->1622 1645 dcoilwtico ≤ 0.631 mae = 0.16 samples = 2 value = -0.323 1621->1645 1623 dcoilwtico ≤ 0.669 mae = 0.006 samples = 4 value = 0.286 1622->1623 1630 dcoilwtico ≤ 0.589 mae = 0.596 samples = 8 value = 0.707 1622->1630 1624 locale_National ≤ 0.5 mae = 0.003 samples = 3 value = 0.287 1623->1624 1629 mae = 0.0 samples = 1 value = 0.273 1623->1629 1625 month_11 ≤ 0.5 mae = 0.001 samples = 2 value = 0.286 1624->1625 1628 mae = 0.0 samples = 1 value = 0.295 1624->1628 1626 mae = 0.0 samples = 1 value = 0.287 1625->1626 1627 mae = 0.0 samples = 1 value = 0.286 1625->1627 1631 month_11 ≤ 0.5 mae = 0.156 samples = 2 value = 0.065 1630->1631 1634 dcoilwtico ≤ 0.616 mae = 0.574 samples = 6 value = 0.92 1630->1634 1632 mae = 0.0 samples = 1 value = -0.091 1631->1632 1633 mae = 0.0 samples = 1 value = 0.22 1631->1633 1635 mae = 0.0 samples = 1 value = 1.845 1634->1635 1636 dcoilwtico ≤ 0.682 mae = 0.465 samples = 5 value = 0.726 1634->1636 1637 dcoilwtico ≤ 0.661 mae = 0.632 samples = 3 value = 1.114 1636->1637 1642 dcoilwtico ≤ 0.699 mae = 0.02 samples = 2 value = 0.707 1636->1642 1638 month_08 ≤ 0.5 mae = 0.688 samples = 2 value = 0.426 1637->1638 1641 mae = 0.0 samples = 1 value = 1.636 1637->1641 1639 mae = 0.0 samples = 1 value = -0.262 1638->1639 1640 mae = 0.0 samples = 1 value = 1.114 1638->1640 1643 mae = 0.0 samples = 1 value = 0.687 1642->1643 1644 mae = 0.0 samples = 1 value = 0.726 1642->1644 1646 mae = 0.0 samples = 1 value = -0.483 1645->1646 1647 mae = 0.0 samples = 1 value = -0.163 1645->1647 1649 month_05 ≤ 0.5 mae = 0.398 samples = 14 value = 0.143 1648->1649 1676 mae = 0.0 samples = 1 value = -0.543 1648->1676 1650 month_04 ≤ 0.5 mae = 0.392 samples = 13 value = 0.106 1649->1650 1675 mae = 0.0 samples = 1 value = 0.584 1649->1675 1651 dcoilwtico ≤ 1.004 mae = 0.401 samples = 10 value = 0.014 1650->1651 1670 dcoilwtico ≤ 0.867 mae = 0.26 samples = 3 value = 0.407 1650->1670 1652 dcoilwtico ≤ 0.983 mae = 0.174 samples = 9 value = -0.079 1651->1652 1669 mae = 0.0 samples = 1 value = 2.37 1651->1669 1653 dcoilwtico ≤ 0.964 mae = 0.182 samples = 8 value = 0.014 1652->1653 1668 mae = 0.0 samples = 1 value = -0.186 1652->1668 1654 month_09 ≤ 0.5 mae = 0.16 samples = 7 value = -0.079 1653->1654 1667 mae = 0.0 samples = 1 value = 0.259 1653->1667 1655 dcoilwtico ≤ 0.877 mae = 0.156 samples = 6 value = -0.112 1654->1655 1666 mae = 0.0 samples = 1 value = 0.106 1654->1666 1656 locale_name_None ≤ 0.5 mae = 0.146 samples = 4 value = 0.05 1655->1656 1663 month_07 ≤ 0.5 mae = 0.085 samples = 2 value = -0.254 1655->1663 1657 mae = 0.0 samples = 1 value = -0.079 1656->1657 1658 month_08 ≤ 0.5 mae = 0.109 samples = 3 value = 0.179 1656->1658 1659 dcoilwtico ≤ 0.792 mae = 0.163 samples = 2 value = 0.017 1658->1659 1662 mae = 0.0 samples = 1 value = 0.182 1658->1662 1660 mae = 0.0 samples = 1 value = -0.146 1659->1660 1661 mae = 0.0 samples = 1 value = 0.179 1659->1661 1664 mae = 0.0 samples = 1 value = -0.339 1663->1664 1665 mae = 0.0 samples = 1 value = -0.169 1663->1665 1671 mae = 0.0 samples = 1 value = 0.88 1670->1671 1672 dcoilwtico ≤ 0.943 mae = 0.153 samples = 2 value = 0.254 1670->1672 1673 mae = 0.0 samples = 1 value = 0.407 1672->1673 1674 mae = 0.0 samples = 1 value = 0.101 1672->1674 1678 dcoilwtico ≤ 0.733 mae = 0.114 samples = 4 value = -0.146 1677->1678 1685 dcoilwtico ≤ 0.991 mae = 0.486 samples = 2 value = 0.608 1677->1685 1679 transferred_False ≤ 0.5 mae = 0.024 samples = 3 value = -0.127 1678->1679 1684 mae = 0.0 samples = 1 value = -0.51 1678->1684 1680 dcoilwtico ≤ 0.693 mae = 0.017 samples = 2 value = -0.11 1679->1680 1683 mae = 0.0 samples = 1 value = -0.164 1679->1683 1681 mae = 0.0 samples = 1 value = -0.093 1680->1681 1682 mae = 0.0 samples = 1 value = -0.127 1680->1682 1686 mae = 0.0 samples = 1 value = 1.093 1685->1686 1687 mae = 0.0 samples = 1 value = 0.122 1685->1687 1689 dcoilwtico ≤ 0.684 mae = 0.301 samples = 2 value = -0.886 1688->1689 1692 dcoilwtico ≤ 0.922 mae = 0.181 samples = 5 value = -0.045 1688->1692 1690 mae = 0.0 samples = 1 value = -0.585 1689->1690 1691 mae = 0.0 samples = 1 value = -1.188 1689->1691 1693 dcoilwtico ≤ 0.695 mae = 0.162 samples = 4 value = 0.023 1692->1693 1700 mae = 0.0 samples = 1 value = -0.303 1692->1700 1694 mae = 0.0 samples = 1 value = -0.045 1693->1694 1695 dcoilwtico ≤ 0.767 mae = 0.171 samples = 3 value = 0.091 1693->1695 1696 mae = 0.0 samples = 1 value = 0.446 1695->1696 1697 dcoilwtico ≤ 0.862 mae = 0.079 samples = 2 value = 0.011 1695->1697 1698 mae = 0.0 samples = 1 value = -0.068 1697->1698 1699 mae = 0.0 samples = 1 value = 0.091 1697->1699 1703 mae = 0.0 samples = 1 value = 0.243 1702->1703 1704 dcoilwtico ≤ 1.047 mae = 0.006 samples = 2 value = 1.345 1702->1704 1705 mae = 0.0 samples = 1 value = 1.338 1704->1705 1706 mae = 0.0 samples = 1 value = 1.351 1704->1706 1708 dcoilwtico ≤ 1.085 mae = 0.06 samples = 4 value = -0.471 1707->1708 1715 month_08 ≤ 0.5 mae = 0.216 samples = 5 value = 0.146 1707->1715 1709 month_08 ≤ 0.5 mae = 0.022 samples = 2 value = -0.42 1708->1709 1712 dcoilwtico ≤ 1.106 mae = 0.039 samples = 2 value = -0.539 1708->1712 1710 mae = 0.0 samples = 1 value = -0.441 1709->1710 1711 mae = 0.0 samples = 1 value = -0.398 1709->1711 1713 mae = 0.0 samples = 1 value = -0.578 1712->1713 1714 mae = 0.0 samples = 1 value = -0.5 1712->1714 1716 dcoilwtico ≤ 1.129 mae = 0.156 samples = 4 value = 0.188 1715->1716 1723 mae = 0.0 samples = 1 value = -0.309 1715->1723 1717 mae = 0.0 samples = 1 value = 0.015 1716->1717 1718 dcoilwtico ≤ 1.211 mae = 0.136 samples = 3 value = 0.23 1716->1718 1719 dcoilwtico ≤ 1.154 mae = 0.162 samples = 2 value = 0.392 1718->1719 1722 mae = 0.0 samples = 1 value = 0.146 1718->1722 1720 mae = 0.0 samples = 1 value = 0.23 1719->1720 1721 mae = 0.0 samples = 1 value = 0.554 1719->1721 1725 mae = 0.0 samples = 1 value = 1.03 1724->1725 1726 dcoilwtico ≤ 0.744 mae = 0.175 samples = 2 value = -0.67 1724->1726 1727 mae = 0.0 samples = 1 value = -0.844 1726->1727 1728 mae = 0.0 samples = 1 value = -0.495 1726->1728 1730 dcoilwtico ≤ 0.578 mae = 0.201 samples = 2 value = -0.011 1729->1730 1733 transferred_False ≤ 0.5 mae = 0.593 samples = 4 value = 2.199 1729->1733 1731 mae = 0.0 samples = 1 value = 0.189 1730->1731 1732 mae = 0.0 samples = 1 value = -0.212 1730->1732 1734 dcoilwtico ≤ 0.805 mae = 0.289 samples = 3 value = 2.257 1733->1734 1739 mae = 0.0 samples = 1 value = 0.753 1733->1739 1735 mae = 0.0 samples = 1 value = 3.008 1734->1735 1736 dcoilwtico ≤ 0.866 mae = 0.058 samples = 2 value = 2.199 1734->1736 1737 mae = 0.0 samples = 1 value = 2.141 1736->1737 1738 mae = 0.0 samples = 1 value = 2.257 1736->1738

Model 4: Sales Prediction with Random Forest & GridsearchCV

In [163]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV

# Choose the type of classifier. 
RFR = RandomForestRegressor()

# Choose some parameter combinations to try
#YOU CAN TRY DIFFERENTS PARAMETERS TO FIND THE BEST MODEL
parameters = {'n_estimators': [5, 10, 100],
              'criterion': ['mse','mae'],
              'max_depth': [5, 10, 15], 
              'min_samples_split': [2, 5, 10],
              'min_samples_leaf': [1,5]
             }

# Type of scoring used to compare parameter combinations

# Run the grid search
grid_obj = GridSearchCV(RFR, parameters,
                        cv=5, #Determines the cross-validation splitting strategy /to specify the number of folds in a (Stratified)KFold
                        n_jobs=-1, #Number of jobs to run in parallel
                        verbose=1)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
RFR = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
RFR.fit(X_train, y_train)
Fitting 5 folds for each of 108 candidates, totalling 540 fits
[Parallel(n_jobs=-1)]: Done 292 tasks      | elapsed:    7.3s
[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:   42.1s finished
Out[163]:
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=15,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=5,
           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False)
In [164]:
print("The best params from gridsearchcv are :" + str(grid_obj.best_params_))
print("The best estimators from gridsearchcv are :" + str(grid_obj.best_estimator_))
The best params from gridsearchcv are :{'min_samples_split': 5, 'n_estimators': 100, 'criterion': 'mse', 'max_depth': 15, 'min_samples_leaf': 1}
The best estimators from gridsearchcv are :RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=15,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=5,
           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False)
In [165]:
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error

predictions = RFR.predict(X_test)
R2_RF_WithGCV_Sale_Pred = r2_score(Y_test, predictions)

print('R2 score = ',R2_RF_WithGCV_Sale_Pred, '/ 1.0')
print('MSE score = ',mean_squared_error(Y_test, predictions), '/ 0.0')
('R2 score = ', 0.6979325035050576, '/ 1.0')
('MSE score = ', 0.30273634666605864, '/ 0.0')
In [166]:
RMSE_RF_WithGCV_Sale_Pred = np.sqrt(mean_squared_error(Y_test, predictions))
In [167]:
print("The mean absolute error of random forest with gridsearchcv is : " + str(mean_absolute_error(Y_test, predictions)))
print("The mean squared error of random forest with gridsearchcv is : " + str(mean_squared_error(Y_test, predictions)))
print("The root mean squared error of random forest with gridsearchcv is : " + str(RMSE_RF_WithGCV_Sale_Pred))
The mean absolute error of random forest with gridsearchcv is : 0.34986893913357636
The mean squared error of random forest with gridsearchcv is : 0.30273634666605864
The root mean squared error of random forest with gridsearchcv is : 0.5502148186536406
In [168]:
#Check and plot the 500 first predictions
plt.plot(Y_test.as_matrix()[0:500], '+', color ='blue', alpha=0.7)
plt.plot(predictions[0:500], 'ro', color ='red', alpha=0.5)
plt.show()

Results

Transaction Prediction: Different Models: Metrics : R-squared </u>

In [169]:
print("Linear regression : R2 score : " + str(R2_Lin_Reg_Tran_Pred))

print("Linear regression with stochastic gradient descent: R2 score : " + str(R2_Sgd_Tran_Pred))

print("Decision Tree without GridsearchCV : R2 score : " + str(R2_DT_WoutGCV_Tran_Pred))

print("Decision Tree with GridsearchCV : R2 score : " + str(R2_DT_WithGCV_Tran_Pred))

print("Random Forest with GridsearchCV : R2 score : "+ str(R2_RF_WithGCV_Tran_Pred))
Linear regression : R2 score : 0.6558644803692054
Linear regression with stochastic gradient descent: R2 score : 0.68279259232856
Decision Tree without GridsearchCV : R2 score : 0.5605361928119182
Decision Tree with GridsearchCV : R2 score : 0.6682680307699073
Random Forest with GridsearchCV : R2 score : 0.7407277036111519

Sales Prediction: Different Models: Metrics: R-Squared</u>

In [170]:
print("Linear regression : R2 score : " + str(R2_Lin_Reg_Sale_Pred))

print("Linear regression with stochastic gradient descent : R2 score : " + str(R2_Sgd_Sale_Pred))

print("Decision Tree without GridsearchCV : R2 score : " + str(R2_DT_WoutGCV_Sale_Pred))

print("Decision Tree with GridsearchCV : R2 score : " + str(R2_DT_WithGCV_Sale_Pred))

print("Random Forest with GridsearchCV : R2 score : "+ str(R2_RF_WithGCV_Sale_Pred))
Linear regression : R2 score : 0.48022163239096594
Linear regression with stochastic gradient descent : R2 score : 0.5011674821308041
Decision Tree without GridsearchCV : R2 score : 0.4291128820755423
Decision Tree with GridsearchCV : R2 score : 0.6164032918669986
Random Forest with GridsearchCV : R2 score : 0.6979325035050576

Transaction Prediction: Different Models: Metrics : RMSE </u>

In [171]:
print("Linear regression : RMSE : " + str(RMSE_Lin_Reg_Tran_Pred))

print("Linear regression with Stochastic Gradient Descent : RMSE : " + str(RMSE_Sgd_Tran_Pred))

print("Decision Tree without GridsearchCV : RMSE : " + str(RMSE_DT_WoutGCV_Tran_Pred))

print("Decision Tree with GridsearchCV : RMSE : " + str(RMSE_DT_WithGCV_Tran_Pred))

print("Random Forest with GridsearchCV : RMSE : "+ str(RMSE_RF_WithGCV_Tran_Pred))
Linear regression : RMSE : 0.5755537381618256
Linear regression with Stochastic Gradient Descent : RMSE : 0.5525769808686058
Decision Tree without GridsearchCV : RMSE : 0.6504032245754516
Decision Tree with GridsearchCV : RMSE : 0.5650863180594958
Random Forest with GridsearchCV : RMSE : 0.4995732617949139

Sales Prediction: Different Models: Metrics: RMSE </u>

In [172]:
print("Linear regression : RMSE : " + str(RMSE_Lin_Reg_Tran_Pred))

print("Linear regression with stochastic gradient descent: RMSE : " + str(RMSE_Sgd_Tran_Pred))

print("Decision Tree without GridsearchCV : RMSE : " + str(RMSE_DT_WoutGCV_Tran_Pred))

print("Decision Tree with GridsearchCV : RMSE : " + str(RMSE_DT_WithGCV_Tran_Pred))

print("Random Forest with GridsearchCV : RMSE : "+ str(RMSE_RF_WithGCV_Tran_Pred))
Linear regression : RMSE : 0.5755537381618256
Linear regression with stochastic gradient descent: RMSE : 0.5525769808686058
Decision Tree without GridsearchCV : RMSE : 0.6504032245754516
Decision Tree with GridsearchCV : RMSE : 0.5650863180594958
Random Forest with GridsearchCV : RMSE : 0.4995732617949139

Conclusion

In [173]:
print("The best regression model for predicting transaction volume & Sales is Random Forest with GridsearchCV")
print("The R2 score of this model for predicting transaction volume is " + str(R2_RF_WithGCV_Tran_Pred))
print("The R2 score of this model for predicting Sales is " + str(R2_RF_WithGCV_Sale_Pred))
print("The RMSE value of this model for predicting transaction volume is " + str(RMSE_RF_WithGCV_Tran_Pred))
print("The RMSE value of this model for predicting sales is " + str(RMSE_RF_WithGCV_Tran_Pred))
The best regression model for predicting transaction volume & Sales is Random Forest with GridsearchCV
The R2 score of this model for predicting transaction volume is 0.7407277036111519
The R2 score of this model for predicting Sales is 0.6979325035050576
The RMSE value of this model for predicting transaction volume is 0.4995732617949139
The RMSE value of this model for predicting sales is 0.4995732617949139